Sample records for earth land surface

Landsurface elevation around the world is reaching new heights—as far as its description and measurement goes. A new global digital elevation model (DEM) is being cited as a significant improvement in the quality of topographic data available for Earth science studies.Landsurface elevation is one of the Earth's most fundamental geophysical properties, but the accuracy and detail with which it has been measured and described globally have been insufficient for many large-area studies. The new model, developed at the U.S. Geological Survey's (USGS) EROS Data Center (EDC), has changed all that.

The European Space Agency (ESA) is defining candidate missions for Earth Observation. In the class of the Earth Explorer missions, dedicated to research and pre-operational demonstration, the LandSurface Processes and Interactions Mission (LSPIM) will acquire the accurate quantitative measurements needed to improve our understanding of the nature and evolution of biosphere-atmosphere interactions and to contribute significantly to a solution of the scaling problems for energy, water and carbon fluxes at the Earth's surface. The mission is intended to provide detailed observations of the surface of the Earth and to collect data related to ecosystem processes and radiation balance. It is also intended to address a range of issues important for environmental monitoring, renewable resources assessment and climate models. The mission involves a dedicated maneuvering satellite which provides multi-directional observations for systematic measurement of LandSurface BRDF (BiDirectional Reflectance Distribution Function) of selected sites on Earth. The satellite carries an optical payload : PRISM (Processes Research by an Imaging Space Mission), a multispectral imager providing reasonably high spatial resolution images (50 m over 50 km swath) in the whole optical spectral domain (from 450 nm to 2.35 μm with a resolution close to 10 nm, and two thermal bands from 8.1 to 9.1 μm). This paper presents the results of the Phase A study awarded by ESA, led by ALCATEL Space Industries and concerning the design of LSPIM.

Land-surface evapotranspiration is shown to strongly influence global fields of rainfall, temperature and motion by calculations using a numerical model of the atmosphere, confirming the general belief in the importance of evapotranspiration-producing surface vegetation for the earth's climate. The current version of the Goddard Laboratory atmospheric general circulation model is used in the present experiment, in which conservation equations for mass, momentum, moisture and energy are expressed in finite-difference form for a spherical grid to calculate (1) surface pressure field evolution, and (2) the wind, temperature, and water vapor fields at nine levels between the surface and a 20 km height.

Planet is an integrated aerospace and data analytics company that operates the largest fleet of Earth-imaging satellites. With more than 140 cube-sats successfully launched to date, Planet is now collecting approximately 10 million square kilometers of imagery per day (3-5m per pixel, in red, green, blue and near infrared spectral bands). By early 2017, Planet's constellation will image the entire landsurface of the Earth on a daily basis. Due to investments in cloud storage and computing, approximately 75% of imagery collected is available to Planet's partners within 24 hours of capture through an Application Program Interface. This unique dataset has enormous applications for monitoring the status of Earth's natural ecosystems, as well as human settlements and agricultural welfare. Through our Ambassadors Program, Planet has made data available for researchers in areas as disparate as human rights monitoring in refugee camps, to assessments of the impact of hydroelectric installations, to tracking illegal gold mining in Amazon forests, to assessing the status of the cryosphere. Here, we share early results from Planet's research partner network, including enhanced spatial and temporal resolution of NDVI data for agricultural health in Saudi Arabia, computation of rates of illegal deforestation in Southern Peru, estimates of tropical forest carbon stocks based on data integration with active sensors, and estimates of glacial flow rates. We synthesize the potentially enormous research and scientific value of Planet's persistent monitoring capability, and discuss methods by which the data will be disseminated into the scientific community.

A one-dimensional reservoir stratification modeling has been developed as part of Model for Scale Adaptive River Transport (MOSART), which is the river transport model used in the Accelerated Climate Modeling for Energy (ACME) and Community Earth System Model (CESM). Reservoirs play an important role in modulating the dynamic water, energy and biogeochemical cycles in the riverine system through nutrient sequestration and stratification. However, most earth system models include lake models that assume a simplified geometry featuring a constant depth and a constant surface area. As reservoir geometry has important effects on thermal stratification, we developed a new algorithm for deriving generic, stratified area-elevation-storage relationships that are applicable at regional and global scales using data from Global Reservoir and Dam database (GRanD). This new reservoir geometry dataset is then used to support the development of a reservoir stratification module within MOSART. The mixing of layers (energy and mass) in the reservoir is driven by eddy diffusion, vertical advection, and reservoir inflow and outflow. Upstream inflow into a reservoir is treated as an additional source/sink of energy, while downstream outflow represented a sink. Hourly atmospheric forcing from North American Land Assimilation System (NLDAS) Phase II and simulated daily runoff by ACME land component are used as inputs for the model over the contiguous United States for simulations between 2001-2010. The model is validated using selected observed temperature profile data in a number of reservoirs that are subject to various levels of regulation. The reservoir stratification module completes the representation of riverine mass and heat transfer in earth system models, which is a major step towards quantitative understanding of human influences on the terrestrial hydrological, ecological and biogeochemical cycles.

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's landsurface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.

The landsurface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the landsurface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse landsurface observations. With the use of newly available landsurface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new landsurface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of landsurface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing landsurface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628

Google's Earth Engine offers a "big data" approach to processing large volumes of NASA and other remote sensing products. h\\ps://earthengine.google.com/ Interfaces include a Javascript or Python-based API, useful for accessing and processing over large periods of record for Landsat and MODIS observations. Other data sets are frequently added, including weather and climate model data sets, etc. Demonstrations here focus on exploratory efforts to perform landsurface change detection related to severe weather, and other disaster events.

LandSurface Temperature and Emissivity (LST&E) data are essential for a wide variety of studies from calculating the evapo-transpiration of plant canopies to retrieving atmospheric water vapor. LST&E products are generated from data acquired by sensors in low Earth orbit (LEO) and by sensors in geostationary Earth orbit (GEO). Although these products represent the same measure, they are produced at different spatial, spectral and temporal resolutions using different algorithms. The different approaches used to retrieve the temperatures and emissivities result in discrepancies and inconsistencies between the different products. NASA has identified a major need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. This poster will introduce the landsurface emissivity product of the NASA MEASUREs project called A Unified and Coherent LandSurface Temperature and Emissivity (LST&E) Earth System Data Record (ESDR). To develop a unified high spectral resolution emissivity database, the MODIS baseline-fit emissivity database (MODBF) produced at the University of Wisconsin-Madison and the ASTER Global Emissivity Database (ASTER GED) produced at JPL will be merged. The unified Emissivity ESDR will be produced globally at 5km in mean monthly time-steps and for 12 bands from 3.6-14.3 micron and extended to 417 bands using a PC regression approach. The poster will introduce this data product. LST&E is a critical ESDR for a wide variety of studies in particular ecosystem and climate modeling.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (approx.10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global landsurface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 10(exp 9) unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a grand challenge to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (˜10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global landsurface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a "grand challenge" to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we havemore » the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.« less

The global water cycle has been profoundly affected by human land-water management. As the changes in the water cycle on land can affect the functioning of a wide range of biophysical and biogeochemical processes of the Earth system, it is essential to represent human land-water management in Earth system models (ESMs). During the recent past, noteworthy progress has been made in large-scale modeling of human impacts on the water cycle but sufficient advancements have not yet been made in integrating the newly developed schemes into ESMs. This study reviews the progresses made in incorporating human factors in large-scale hydrological models and their integration into ESMs. The study focuses primarily on the recent advancements and existing challenges in incorporating human impacts in global landsurface models (LSMs) as a way forward to the development of ESMs with humans as integral components, but a brief review of global hydrological models (GHMs) is also provided. The study begins with the general overview of human impacts on the water cycle. Then, the algorithms currently employed to represent irrigation, reservoir operation, and groundwater pumping are discussed. Next, methodological deficiencies in current modeling approaches and existing challenges are identified. Furthermore, light is shed on the sources of uncertainties associated with model parameterizations, grid resolution, and datasets used for forcing and validation. Finally, representing human land-water management in LSMs is highlighted as an important research direction toward developing integrated models using ESM frameworks for the holistic study of human-water interactions within the Earths system.

A new physically based runoff routing model, called the Model for Scale Adaptive River Transport (MOSART), has been developed to be applicable across local, regional, and global scales. Within each spatial unit, surface runoff is first routed across hillslopes and then discharged along with subsurface runoff into a ‘‘tributary subnetwork’’ before entering the main channel. The spatial units are thus linked via routing through the main channel network, which is constructed in a scale-consistent way across different spatial resolutions. All model parameters are physically based, and only a small subset requires calibration.MOSART has been applied to the Columbia River basinmore » at 1/ 168, 1/ 88, 1/ 48, and 1/ 28 spatial resolutions and was evaluated using naturalized or observed streamflow at a number of gauge stations. MOSART is compared to two other routing models widely used with landsurface models, the River Transport Model (RTM) in the Community Land Model (CLM) and the Lohmann routing model, included as a postprocessor in the Variable Infiltration Capacity (VIC) model package, yielding consistent performance at multiple resolutions. MOSART is further evaluated using the channel velocities derived from field measurements or a hydraulic model at various locations and is shown to be capable of producing the seasonal variation and magnitude of channel velocities reasonably well at different resolutions. Moreover, the impacts of spatial resolution on model simulations are systematically examined at local and regional scales. Finally, the limitations ofMOSART and future directions for improvements are discussed.« less

LandSurface Temperature (LST) is a good indicator of the surface energy balance because it is determined by interactions and energy fluxes between the atmosphere and the ground. The variability of landsurface properties and vegetation densities across the Earth's surface changes these interactions and gives LST a unique biogeographic influence. Natural and human-induced disturbances modify the surface characteristics and alter the expression of LST. This results in a heterogeneous and dynamic thermal environment. Measurements that merge these factors into a single global metric, while maintaining the important biophysical and biogeographical factors of the landsurface's thermal environment are needed to better understand integrated temperature changes in the Earth system. Using satellite-based LST we have developed a new global metric that focuses on one critical component of LST that occurs when the relationship between vegetation density and surface temperature is strongly coupled: annual maximum LST (LSTmax). A 10 year evaluation of LSTmax histograms that include every 1-km pixel across the Earth's surface reveals that this integrative measurement is strongly influenced by the biogeographic patterns of the Earth's ecosystems, providing a unique comparative view of the planet every year that can be likened to the Earth's thermal maximum fingerprint. The biogeographical component is controlled by the frequency and distribution of vegetation types across the Earth's landsurface and displays a trimodal distribution. The three modes are driven by ice covered polar regions, forests, and hot desert/shrubland environments. In ice covered areas the histograms show that the heat of fusion results in a convergence of surface temperatures around the melting point. The histograms also show low interannual variability reflecting two important global landsurface dynamics; 1) only a small fraction of the Earth's surface is disturbed in any given year, and 2) when

The CRUTEM4 (Climatic Research Unit Temperature, version 4) land-surface air temperature data set is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this data set: online access to monthly, seasonal and annual data values and time series graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 data set. A mathematical description of the construction of the CRUTEM4 data set (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the data set used in a particular study. The CRUTEM4 data set used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

Information about landsurface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these landsurface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Landsurface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of landsurface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of landsurface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Landsurface data assimilation aims to utilize both our landsurface process knowledge, as embodied in a landsurface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land

Technologies for imaging the surface of the Earth, through satellite based Earth observations (EO) have enormously evolved over the past 50 years. The trends are likely to evolve further as the user community increases and their awareness and demands for EO data also increases. In this review paper, a development trend on EO imaging systems is presented with the objective of deriving the evolving patterns for the EO user community. From the review and analysis of medium-to-high resolution EO-based land-surface sensor missions, it is observed that there is a predictive pattern in the EO evolution trends such that every 10-15 years, more sophisticated EO imaging systems with application specific capabilities are seen to emerge. Such new systems, as determined in this review, are likely to comprise of agile and small payload-mass EO landsurface imaging satellites with the ability for high velocity data transmission and huge volumes of spatial, spectral, temporal and radiometric resolution data. This availability of data will magnify the phenomenon of ;Big Data; in Earth observation. Because of the ;Big Data; issue, new computing and processing platforms such as telegeoprocessing and grid-computing are expected to be incorporated in EO data processing and distribution networks. In general, it is observed that the demand for EO is growing exponentially as the application and cost-benefits are being recognized in support of resource management.

The objectives of this study are as follows: (1) develop a landing option such that it is a viable trade option for future NASA missions; (2) provide NASA programs with solid technical support in the landing systems area; (3) develop the technical staff; and (4) advance the state of landing systems technology to apply to future NASA missions. All results are presented in viewgraph format.

The interplay of human actions and natural processes over varied spatial and temporal scales can result in abrupt transitions between contrasting landsurface states. Understanding these transitions is a key goal of sustainability science because they can represent abrupt losses of natural capital. This paper recognizes flickering between alternate landsurface states in advance of threshold change and critical slowing down in advance of both threshold changes and noncritical transformation. The early warning signals we observe are rises in autocorrelation, variance, and skewness within millimeter-resolution thickness measurements of tephra layers deposited in A.D. 2010 and A.D. 2011. These signals reflect changing patterns of surface vegetation, which are known to provide early warning signals of critical transformations. They were observed toward migrating soil erosion fronts, cryoturbation limits, and expanding deflation zones, thus providing potential early warning signals of landsurface change. The record of the spatial patterning of vegetation contained in contemporary tephra layers shows how proximity to landsurface change could be assessed in the widespread regions affected by shallow layers of volcanic fallout (those that can be subsumed within the existing vegetation cover). This insight shows how we could use tephra layers in the stratigraphic record to identify “near misses,” close encounters with thresholds that did not lead to tipping points, and thus provide additional tools for archaeology, sustainability science, and contemporary land management. PMID:23530230

The LANDSAT program and system is described. The entire global landsurface of Earth is visualized in 400 color plates at a scale and resolution that specify natural land cultural features in man's familiar environments. A glossary is included.

SURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surfaces: nature, town, inland water and ocean. It is mostly based on pre-existing, well-validated scientific models that are continuously improved. The motivation for the building of SURFEX is to use strictly identical scientific models in a high range of applications in order to mutualise the research and development efforts. SURFEX can be run in offline mode (0-D or 2-D runs) or in coupled mode (from mesoscale models to numerical weather prediction and climate models). An assimilation mode is included for numerical weather prediction and monitoring. In addition to momentum, heat and water fluxes, SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. The main principles of the organisation of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally, the main applications of the code are summarised. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage.

Reviews the tremendous transformation that human beings have wrought on the earth's surface from the Holocene to the present. Traces this transformation through various stages: the emergence and development of agriculture, agricultural impact and land degradation, ecological and political imperialism, industrialization, and environmental…

Certain vegetation types (e.g., deciduous shrubs, deciduous trees, grasslands) have distinct life cycles marked by the growth and senescence of leaves and periods of enhanced photosynthetic activity. Where these types exist, recurring changes in foliage alter the reflectance of electromagnetic radiation from the landsurface, which can be measured using remote sensors. The timing of these recurring changes in reflectance is called landsurface phenology (LSP). During recent decades, a variety of methods have been used to derive LSP metrics from time series of reflectance measurements acquired by satellite-borne sensors. In contrast to conventional phenology observations, LSP metrics represent the timing of reflectance changes that are driven by the aggregate activity of vegetation within the areal unit measured by the satellite sensor and do not directly provide information about the phenology of individual plants, species, or their phenophases. Despite the generalized nature of satellite sensor-derived measurements, they have proven useful for studying changes in LSP associated with various phenomena. This chapter provides a detailed overview of the use of satellite remote sensing to monitor LSP. First, the theoretical basis for the application of satellite remote sensing to the study of vegetation phenology is presented. After establishing a theoretical foundation for LSP, methods of deriving and validating LSP metrics are discussed. This chapter concludes with a discussion of major research findings and current and future research directions.

To increase our understanding of how humans have altered the Earth's surface and to facilitate landsurface modeling experiments aimed to elucidate the direct impact of land cover change on the Earth system, we create and analyze a database of global land use/cover change (LUCC). From a combination of sources including satellite imagery and other remote sensing, ecological modeling, and country surveys, we adapt and synthesize existing maps of potential land cover and layers of the major anthropogenic land covers, including a layer of wetland loss, that are then tailored for landsurface modeling studies. Our map database shows that anthropogenic land cover totals to approximately 40% of the Earth's surface, consistent with literature estimates. Almost all (92%) of the natural grassland on the Earth has been converted to human use, mostly grazing land, and the natural temperate savanna with mixed C3/C4 is almost completely lost (˜90%), due mostly to conversion to cropland. Yet the resultant change in functioning, in terms of plant functional types, of the Earth system from land cover change is dominated by a loss of tree cover. Finally, we identify need for standardization of percent bare soil for global land covers and for a global map of tree plantations. Estimates of land cover change are inherently uncertain, and these uncertainties propagate into modeling studies of the impact of land cover change on the Earth system; to begin to address this problem, modelers need to document fully areas of land cover change used in their studies.

A constellation of satellites that routinely and frequently images the Earth's landsurface in consistently calibrated wavelengths from the visible through the microwave and in spatial detail that ranges from sub-meter to hundreds of meters would offer enormous potential benefits to society. A well-designed and effectively operated landsurface imaging satellite constellation could have great positive impact not only on the quality of life for citizens of all nations, but also on mankind's very ability to sustain life as we know it on this planet long into the future. The primary objective of the Committee on Earth Observation Satellites (CEOS) LandSurface Imaging (LSI) Constellation is to define standards (or guidelines) that describe optimal future LSI Constellation capabilities, characteristics, and practices. Standards defined for a LSI Constellation will be based on a thorough understanding of user requirements, and they will address at least three fundamental areas of the systems comprising a LandSurface Imaging Constellation: the space segments, the ground segments, and relevant policies and plans. Studies conducted by the LSI Constellation Study Team also will address current and shorter-term problems and issues facing the land remote sensing community today, such as seeking ways to work more cooperatively in the operation of existing landsurface imaging systems and helping to accomplish tangible benefits to society through application of landsurface image data acquired by existing systems. 2007 LSI Constellation studies are designed to establish initial international agreements, develop preliminary standards for a mid-resolution landsurface imaging constellation, and contribute data to a global forest assessment.

Two full-scale passive Earth Entry Vehicles (EEV) with realistic structure, surrogate sample container, and surrogate Thermal Protection System (TPS) were built at NASA Langley Research Center (LaRC) and tested at the Utah Test and Training Range (UTTR). The main test objective was to demonstrate structural integrity and investigate possible impact response deviations of the realistic vehicle as compared to rigid penetrometer responses. With the exception of the surrogate TPS and minor structural differences in the back shell construction, the two test vehicles were identical in geometry and both utilized the Integrated Composite Stiffener Structure (ICoSS) structural concept in the forward shell. The ICoSS concept is a lightweight and highly adaptable composite concept developed at NASA LaRC specifically for entry vehicle TPS carrier structures. The instrumented test vehicles were released from a helicopter approximately 400 m above ground. The drop height was selected such that at least 98% of the vehicles terminal velocity would be achieved. While drop tests of spherical penetrometers and a low fidelity aerodynamic EEV model were conducted at UTTR in 1998 and 2000, this was the first time a passive EEV with flight-like structure, surrogate TPS, and sample container was tested at UTTR for the purpose of complete structural system validation. Test results showed that at a landing vertical speed of approximately 30 m/s, the test vehicle maintained structural integrity and enough rigidity to penetrate the sandy clay surface thus attenuating the landing load, as measured at the vehicle CG, to less than 600 g. This measured deceleration was found to be in family with rigid penetrometer test data from the 1998 and 2000 test campaigns. Design implications of vehicle structure/soil interaction with respect to sample container and sample survivability are briefly discussed.

This paper resurrects a version of Poisson's Partial Differential Equation (PDE) associated with the gravitational field at the Earth's surface and illustrates how the PDE possesses a capability to extract the mass density of Earth's topography from land-based gravity data. Herein, first we propound a theorem which mathematically introduces this version of Poisson's PDE adapted for the Earth's surface and then we use this PDE to develop a method of approximating the terrain mass density. Also, we carry out a real case study showing how the proposed approach is able to be applied to a set of land-based gravity data. In the case study, the method is summarized by an algorithm and applied to a set of gravity stations located along a part of the north coast of the Persian Gulf in the south of Iran. The results were numerically validated via rock-samplings as well as a geological map. Also, the method was compared with two conventional methods of mass density reduction. The numerical experiments indicate that the Poisson PDE at the Earth's surface has the capability to extract the mass density from land-based gravity data and is able to provide an alternative and somewhat more precise method of estimating the terrain mass density.

A brief discussion of the development of the Apollo earthlanding system and a functional description of the system are presented in this report. The more significant problems that were encountered during the program, the solutions, and, in general, the knowledge that was gained are discussed in detail. Two appendixes presenting a detailed description of the various system components and a summary of the development and the qualification test programs are included.

We developed a coupled regional climate system model based on the CCLM regional climate model. Within this model system, using OASIS3-MCT as a coupler, CCLM can be coupled to two landsurface models (the Community Land Model (CLM) and VEG3D), the NEMO-MED12 regional ocean model for the Mediterranean Sea, two ocean models for the North and Baltic seas (NEMO-NORDIC and TRIMNP+CICE) and the MPI-ESM Earth system model.We first present the different model components and the unified OASIS3-MCT interface which handles all couplings in a consistent way, minimising the model source code modifications and defining the physical and numerical aspects of the couplings. We also address specific coupling issues like the handling of different domains, multiple usage of the MCT library and exchange of 3-D fields.We analyse and compare the computational performance of the different couplings based on real-case simulations over Europe. The usage of the LUCIA tool implemented in OASIS3-MCT enables the quantification of the contributions of the coupled components to the overall coupling cost. These individual contributions are (1) cost of the model(s) coupled, (2) direct cost of coupling including horizontal interpolation and communication between the components, (3) load imbalance, (4) cost of different usage of processors by CCLM in coupled and stand-alone mode and (5) residual cost including i.a. CCLM additional computations.Finally a procedure for finding an optimum processor configuration for each of the couplings was developed considering the time to solution, computing cost and parallel efficiency of the simulation. The optimum configurations are presented for sequential, concurrent and mixed (sequential+concurrent) coupling layouts. The procedure applied can be regarded as independent of the specific coupling layout and coupling details.We found that the direct cost of coupling, i.e. communications and horizontal interpolation, in OASIS3-MCT remains below 7 % of the CCLM stand

The energy content of the Earth system is determined by the balance or imbalance between the incoming energy from solar radiation and the outgoing energy of terrestrial long wavelength radiation. Change in the Earth system energy budget is the ultimate cause of global climate change. Satellite data show that there is a small yet persistent radiation imbalance at the top-of-atmosphere such that Earth has been steadily accumulating energy, consistent with the theory of greenhouse effect. It is commonly believed [IPCC, 2001; 2007] that up to 94% of the energy trapped by anthropogenic greenhouse gases is absorbed by the upper several hundred meter thick layer of global oceans, with the remaining to accomplish ice melting, atmosphere heating, and land warming, etc. However, the recent measurements from ocean monitoring system indicated that the rate of oceanic heat uptake has not kept pace with the greenhouse heat trapping rate over the past years [Trenberth and Fasullo, Science, 328: 316-317, 2010]. An increasing amount of energy added to the earth system has become unaccounted for, or is missing. A recent study [Loeb et al., Nature Geoscience, 5:110-113, 2012] suggests that the missing energy may be located in the deep ocean down to 1,800 m. Here we show that at least part of the missing energy can be alternatively explained by the land mass warming. We argue that the global continents alone should have a share greater than 10% of the global warming energy. Although the global lands reflect solar energy at a higher rate, they use less energy for evaporation than do the oceans. Taken into accounts the terrestrial/oceanic differences in albedo (34% vs. 28%) and latent heat (27% vs. 58% of net solar radiation at the surface), the radiative energy available per unit surface area for storage or other internal processes is more abundant on land than on ocean. Despite that the lands cover only about 29% of the globe, the portion of global warming energy stored in the lands

Spatially detailed and temporally dynamic land use land cover data is necessary to monitor the state of the landsurface for various applications. Yet, such data at a continental to global scale is lacking. Here, we developed high resolution (30 meter) annual land use land cover layers for the continental Africa using Google Earth Engine. To capture ground truth training data, high resolution satellite imageries were visually inspected and used to identify 7, 212 sample Landsat pixels that were comprised entirely of one of seven land use land cover classes (water, man-made impervious surface, high biomass, low biomass, rock, sand and bare soil). For model validation purposes, 80% of points from each class were used as training data, with 20% withheld as a validation dataset. Cloud free Landsat 7 annual composites for 2000 to 2015 were generated and spectral bands from the Landsat images were then extracted for each of the training and validation sample points. In addition to the Landsat spectral bands, spectral indices such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used as covariates in the model. Additionally, calibrated night time light imageries from the National Oceanic and Atmospheric Administration (NOAA) were included as a covariate. A decision tree classification algorithm was applied to predict the 7 land cover classes for the periods 2000 to 2015 using the training dataset. Using the validation dataset, classification accuracy including omission error and commission error were computed for each land cover class. Model results showed that overall accuracy of classification was high (88%). This high resolution land cover product developed for the continental Africa will be available for public use and can potentially enhance the ability of monitoring and studying the state of the Earth's surface.

LVT is a framework developed to provide an automated, consolidated environment for systematic landsurface model evaluation Includes support for a range of in-situ, remote-sensing and other model and reanalysis products. Supports the analysis of outputs from various LIS subsystems, including LIS-DA, LIS-OPT, LIS-UE. Note: The Land Information System Verification Toolkit (LVT) is a NASA software tool designed to enable the evaluation, analysis and comparison of outputs generated by the Land Information System (LIS). The LVT software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.1 or later. Land Information System Verification Toolkit (LVT) NOSA.

The understanding of landsurface processes and their parameterization in atmospheric, hydrologic, and ecosystem models has been a dominant research theme over the past decade. For example, many studies have demonstrated the key role of land cover characteristics as controlling factors in determining landsurface processes, such as the exchange of water, energy, carbon, and trace gases between the landsurface and the lower atmosphere. The requirements for multiresolution land cover characteristics data to support coupled-systems modeling have also been well documented, including the need for data on land cover type, land use, and many seasonally variable land cover characteristics, such as albedo, leaf area index, canopy conductance, surface roughness, and net primary productivity. Recently, the developers of land data have worked more closely with the landsurface process modelers in these efforts.

The current research provides a method for tracking near-surface mass-density anomalies via using only land-based gravity data, which is based on a special version of Poisson's Partial Differential Equation (PDE) of the gravitational field at Earth's surface. The research demonstrates how the Poisson's PDE can provide us with a capability to extract the near-surface mass-density anomalies from land-based gravity data. Herein, this version of the Poisson's PDE is mathematically introduced to the Earth's surface and then it is used to develop the new method for approximating the mass-density via derivatives of the Earth's gravitational field (i.e. via the gradient tensor). Herein, the author believes that the PDE can give us new knowledge about the behavior of the Earth's gravitational field at the Earth's surface which can be so useful for developing new methods of Earth's mass-density determination. In a case study, the proposed method is applied to a set of gravity stations located in the south of Iran. The results were numerically validated via certain knowledge about the geological structures in the area of the case study. Also, the method was compared with two standard methods of mass-density determination. All the numerical experiments show that the proposed approach is well-suited for tracking near-surface mass-density anomalies via using only the gravity data. Finally, the approach is also applied to some petroleum exploration studies of salt diapirs in the south of Iran.

This paper examines the utilization of surface temperature as a variable to be assimilated in offline landsurface hydrological models. Comparisons between the model computed and satellite observed surface temperatures have been carried out. The assimilation of surface temperature is carried out twice a day (corresponding to the AM and PM overpass of the NOAA10) over the Red- Arkansas basin in the Southwestern United States (31 deg 50 min N - 36 deg N, 94 deg 30 min W - 104 deg 30 min W) for a period of one year (August 1987 to July 1988). The effect of assimilation is to reduce the difference between the surface soil moisture computed for the precipitation and/or shortwave radiation perturbed case and the unperturbed case compared to no assimilation.

This paper examines the utilization of surface temperature as a variable to be assimilated in offline landsurface hydrological models. Comparisons between the model computed and satellite observed surface temperatures have been carried out. The assimilation of surface temperature is carried out twice a day (corresponding to the AM and PM overpass of the NOAA10) over the Red-Arkansas basin in the Southwestern United States (31 degs 50 sec N - 36 degrees N, 94 degrees 30 seconds W - 104 degrees 3 seconds W) for a period of one year (August 1987 to July 1988). The effect of assimilation is to reduce the difference between the surface soil moisture computed for the precipitation and/or shortwave radiation perturbed case and the unperturbed case compared to no assimilation.

Mathematical model approximates microwave radiation emitted by landsurfaces traveling to microwave radiometer in outer space. Applied to measurements made by Scanning Multichannel Microwave Radiometer (SMMR). Developed for interpretation of microwave imagery of Earth to obtain distributions of various chemical, physical, and biological characteristics across its surface. Intended primarily for use in mapping moisture content of soil and fraction of Earth covered by vegetation. Advanced Very-High-Resolution Radiometer (AVHRR), provides additional information on vegetative cover, thereby making possible retrieval of soil-moisture values from SMMR measurements. Possible to monitor changes of landsurface during intervals of 5 to 10 years, providing significant data for mathematical models of evolution of climate.

Hydrological cycle is the perpetual movement of water throughout the various component of the global Earth's system. Focusing on the landsurface component of this cycle, the determination of the succession of dry and humid periods is of high importance with respect to water resources management but also with respect to global geochemical cycles. This knowledge requires a specified estimation of recent fluctuations of the landsurface cycle at continental and global scales. Our approach leans towards a new estimation of freshwater discharge to oceans from 1875 to 1994 as recently proposed by Labat et al. [Labat, D., Goddéris, Y., Probst, JL, Guyot, JL, 2004. Evidence for global runoff increase related to climate warming. Advances in Water Resources, 631-642]. Wavelet analyses of the annual freshwater discharge time series reveal an intermittent multiannual variability (4- to 8-y, 14- to 16-y and 20- to 25-y fluctuations) and a persistent multidecadal 30- to 40-y variability. Continent by continent, reasonable relationships between land-water cycle oscillations and climate forcing (such as ENSO, NAO or sea surface temperature) are proposed even though if such relationships or correlations remain very complex. The high intermittency of interannual oscillations and the existence of persistent multidecadal fluctuations make prediction difficult for medium-term variability of droughts and high-flows, but lead to a more optimistic diagnostic for long-term fluctuations prediction.

The website information describing the forcing meteorological data used for the landsurface model (LSM) simulation, which were observed at an Automated Meteorological Station CAWS) at the Sapporo District Meteorological Observatory maintained by the Japan Meteorological Agency (JMA), was missing from the text. The 1-hourly data were obtained from the website of Kisyoutoukeijouhou (Information for available JMA-observed meteorological data in the past) on the website of JMA (in Japanese) (available at: http://www.jma.go.jpijmaimenulreport.html). The measurement height information of 59.5 m for the anemometer at the Sapporo Observatory was also obtained from the website of JMA (in Japanese) (available at: http://www.jma.go.jp/jma/menu/report.html). In addition, the converted 10-m wind speed, based on the AWS/JMA data, was further converted to a 2-m wind speed prior to its use with the land model as a usual treatment of off-line Catchment simulation. Please ignore the ice absorption data on the website mentioned in paragraph [15] which was not used for our calculations (but the data on the website was mostly the same as the estimated ice absorption coefficients by the following method because they partially used the same data by Warren [1984]). We calculated the ice absorption coefficients with the method mentioned in the same paragraph, for which some of the refractive index data by Warren [1984] were used and then interpolated between wavelengths, and also mentioned in paragraph [20] for the visible (VIS) and near-infrared (NIR) ranges. The optical data we used were interpolated between wavelengths as necessary.

Projecting future biosphere-climate feedbacks using Earth system models (ESMs) relies heavily on robust modeling of landsurface carbon dynamics. More importantly, soil nutrient (particularly, nitrogen (N) and phosphorus (P)) dynamics strongly modulate carbon dynamics, such as plant sequestration of atmospheric CO2. Prevailing ESM land models all consider nitrogen as a potentially limiting nutrient, and several consider phosphorus. However, including nutrient cycle processes in ESM land models potentially introduces large uncertainties that could be identified and addressed by improved observational constraints. We describe the development of two nutrient cycle benchmarks for ESM land models: (1) nutrient partitioning between plants and soil microbes inferred from 15N and 33P tracers studies and (2) nutrient limitation effects on carbon cycle informed by long-term fertilization experiments. We used these benchmarks to evaluate critical hypotheses regarding nutrient cycling and their representation in ESMs. We found that a mechanistic representation of plant-microbe nutrient competition based on relevant functional traits best reproduced observed plant-microbe nutrient partitioning. We also found that for multiple-nutrient models (i.e., N and P), application of Liebig's law of the minimum is often inaccurate. Rather, the Multiple Nutrient Limitation (MNL) concept better reproduces observed carbon-nutrient interactions.

Earthsurface processes, landforms and sediment deposits are intimately related - involving erosion of rocks, generation of sediment, and transport and deposition of sediment through various Earthsurface environments. These processes, and the landforms and deposits that they generate, have a fundamental bearing on engineering, environmental and public safety issues; on recovery of economic resources; and on our understanding of Earth history. This unique textbook brings together the traditional disciplines of sedimentology and geomorphology to explain Earthsurface processes, landforms and sediment deposits in a comprehensive and integrated way. It is the ideal resource for a two-semester course in sedimentology, stratigraphy, geomorphology, and Earthsurface processes from the intermediate undergraduate to beginning graduate level. The book is also accompanied by a website hosting illustrations and material on field and laboratory methods for measuring, describing and analyzing Earthsurface processes, landforms and sediments.

The U.S. Geological Survey's (USGS) EROS Data Center (EDC) has managed the Landsat data archive for more than two decades. This archive provides a rich collection of information about the Earth's landsurface. Major changes to the surface of the planet can be detected, measured, and analyzed using Landsat data. The effects of desertification, deforestation, pollution, cataclysmic volcanic activity, and other natural and anthropogenic events can be examined using data acquired from the Landsat series of Earth-observing satellites. The information obtainable from the historical and current Landsat data play a key role in studying surface changes through time. This document provides an overview of the Landsat program and illustrates the application of the data to monitor changes occurring on the surface of the Earth. To reveal changes that have taken place within the past 20 years, pairs and triplicates of images were constructed from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Landsat MSS data provide a historical record of the Earth's landsurface from the early 1970's to the early 1990's. Landsat TM data provide landsurface information from the early 1980's to the present.

Knowledge of landsurface processes is important to many real-world applications such as agricultural production, water resources management, and flood predication. The Air Force Weather Agency (AFWA) has provided the USDA and other customers global soil moisture and temperature data for the past 30 years using the agrometeorological data assimilation model (now called AGRMET), merging atmospheric data. Further, accurate initialization of landsurface conditions has been shown to greatly influence and improve weather forecast model and seasonal-to-interannual climate predictions. The AFWA AGRMET model exploits real time precipitation observations and analyses, global forecast model and satellite data to generate global estimates of soil moisture, soil temperature and other landsurface states at 48km spatial resolution. However, to truly address the landsurface initialization and climate prediction problem, and to mitigate the errors introduced by the differences in spatial scales of models, representations of landsurface conditions need to be developed at the same fine scales such as that of cloud resolving models. NASA's Goddard Space Flight Center has developed an offline land data assimilation system known as the Land Information System (LIS) capable of modeling land atmosphere interactions at spatial resolutions as fine as 1km. LIS provides a software architecture that integrates the use of the state of the art landsurface models, data assimilation techniques, and high performance computing and data management tools. LIS also employs many high resolution surface parameters such as the NASA Earth Observing System (EOS)-era products. In this study we describe the development of a next generation high resolution landsurface modeling and data assimilation system, combining the capabilities of LIS and AGRMET. We investigate the influence of high resolution landsurface data and observations on the landsurface conditions by comparing with the operational AGRMET

Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and landsurface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed landsurface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed landsurface temperature are statistically significant. The relationships between the maximum air temperature and daytime landsurface temperature depends significantly on landsurface types and vegetation index, but the minimum air temperature and nighttime landsurface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS landsurface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS landsurface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C).

There are numerous reports in the literature of observations of landsurface temperatures. Some of these, almost all made in situ, reveal maximum values in the 50°-70°C range, with a few, made in desert regions, near 80°C. Consideration of a simplified form of the surface energy balance equation, utilizing likely upper values of absorbed shortwave flux (1000 W m2) and screen air temperature (55°C), that surface temperatures in the vicinity of 90°-100°C may occur for dry, darkish soils of low thermal conductivity (0.1-0.2 W m1 K1). Numerical simulations confirm this and suggest that temperature gradients in the first few centimeters of soil may reach 0.5°-1°C mm1 under these extreme conditions. The study bears upon the intrinsic interest of identifying extreme maximum temperatures and yields interesting information regarding the comfort zone of animals (including man).

Predictions of climate change depend on accurately modeling the feedbacks among the carbon cycle, nitrogen cycle, and climate system. Several global landsurface models have shown that nitrogen limitation determines how land carbon fluxes respond to rising CO2, nitrogen deposition, and climate change, thereby influencing predictions of climate change. However, the magnitude of the carbon-nitrogen-climate feedbacks varies considerably by model, leading to critical and timely questions of why they differ and how they compare to field observations. To address these questions, we initiated a model inter-comparison of spatial patterns and drivers of nitrogen limitation. The experiment assessed the regional consequences of sustained nitrogen additions in a set of 25-year global nitrogen fertilization simulations. The model experiments were designed to cover effects from small changes in nitrogen inputs associated with plausible increases in nitrogen deposition to large changes associated with field-based nitrogen fertilization experiments. The analyses of model simulations included assessing the geographically varying degree of nitrogen limitation on plant and soil carbon cycling and the mechanisms underlying model differences. Here, we present results from two global land-surface models (CLM-CN and O-CN) with differing approaches to modeling carbon-nitrogen interactions. The predictions from each model were compared to a set of globally distributed observational data that includes nitrogen fertilization experiments, 15N tracer studies, small catchment nitrogen input-output studies, and syntheses across nitrogen deposition gradients. Together these datasets test many aspects of carbon-nitrogen coupling and are able to differentiate between the two models. Overall, this study is the first to explicitly benchmark carbon and nitrogen interactions in Earth System Models using a range of observations and is a foundation for future inter-comparisons.

A detailed report has recently been published in which the whole subject of mine reclamation has been extensively reviewed and discussed. Part One deals with the technology of reclamation, in which the methods and procedures used have been illustrated by examples taken from the practice of different countries as required by law or by accepted usage. In general, the illustrations used are the most stringent that apply to the procedure under discussion. This serves to show the situation in its most severe light, but it also gives warning of the direction in which the law will move in other countriesmore » that are not so environmentally conscious as the pacesetters. Part Two of the report deals with the law and practice in the major mining nations of the West that have legislation on the subject. This is a field in which much movement is taking place; new laws and regulations are being enacted, and old ones amended and revised. The laws outlined in the report are designed to give the general sense of the law and define the most important regulations. Any mining company contemplating surface mining in a country with which it is unfamiliar will naturally obtain the currently valid legislation. Reclamation of Surface Mined Lands by W.L.G. Muir (price US $225 or equivalent) can be obtained from World Coal, Book Department, 500 Howard Street, San Francisco, California 94105, USA.« less

Land-based demand for food and fibre is projected to increase in the future. In light of global sustainability challenges only part of this increase will be met by expansion of land use into relatively untouched regions. Additional demand will have to be fulfilled by intensification and other adjustments in management of land that already is under agricultural and forestry use. Such land management today occurs on about half of the ice-free landsurface, as compared to only about one quarter that has undergone a change in land cover. As the number of studies revealing substantial biogeophysical and biogeochemical effects of land management is increasing, moving beyond land cover change towards including land management has become a key focus for Earth system modeling. However, a basis for prioritizing land management activities for implementation in models is lacking. We lay this basis for prioritization in a collaborative project across the disciplines of Earth system modeling, land system science, and Earth observation. We first assess the status and plans of implementing land management in Earth system and dynamic global vegetation models. A clear trend towards higher complexity of land use representation is visible. We then assess five criteria for prioritizing the implementation of land management activities: (1) spatial extent, (2) evidence for substantial effects on the Earth system, (3) process understanding, (4) possibility to link the management activity to existing concepts and structures of models, (5) availability of data required as model input. While the first three criteria have been assessed by an earlier study for ten common management activities, we review strategies for implementation in models and the availability of required datasets. We can thus evaluate the management activities for their performance in terms of importance for the Earth system, possibility of technical implementation in models, and data availability. This synthesis reveals

Land-based demand for food and fibre is projected to increase in the future. In light of global sustainability challenges only part of this increase will be met by expansion of land use into relatively untouched regions. Additional demand will have to be fulfilled by intensification and other adjustments in management of land that already is under agricultural and forestry use. Such land management today occurs on about half of the ice-free landsurface, as compared to only about one quarter that has undergone a change in land cover. As the number of studies revealing substantial biogeophysical and biogeochemical effects of land management is increasing, moving beyond land cover change towards including land management has become a key focus for Earth system modeling. However, a basis for prioritizing land management activities for implementation in models is lacking. We lay this basis for prioritization in a collaborative project across the disciplines of Earth system modeling, land system science, and Earth observation. We first assess the status and plans of implementing land management in Earth system and dynamic global vegetation models. A clear trend towards higher complexity of land use representation is visible. We then assess five criteria for prioritizing the implementation of land management activities: (1) spatial extent, (2) evidence for substantial effects on the Earth system, (3) process understanding, (4) possibility to link the management activity to existing concepts and structures of models, (5) availability of data required as model input. While the first three criteria have been assessed by an earlier study for ten common management activities, we review strategies for implementation in models and the availability of required datasets. We can thus evaluate the management activities for their performance in terms of importance for the Earth system, possibility of technical implementation in models, and data availability. This synthesis reveals

This booklet provides an overview of the Landsat program and shows the application of the data to monitor changes occurring on the surface of the Earth. To show changes that have taken place within the last 20 years or less, image pairs were constructed from the Landsat multispectral scanner (MSS) and thematic mapper (TM) sensors. Landsat MSS data provide a historical global record of the landsurface from the early 1970's to present. Landsat TM data provide landsurface information from the early 1980's to present.

The Western EarthSurface Processes Team (WESPT) of the U.S. Geological Survey (USGS) conducts geologic mapping, earth-surface process investigations, and related topical earth science studies in the western United States. This work is focused on areas where modern geologic maps and associated earth-science data are needed to address key societal and environmental issues such as ground-water quality, landslides and other potential geologic hazards, and land-use decisions. Areas of primary emphasis in 2006 included southern California, the San Francisco Bay region, the Mojave Desert, the Colorado Plateau region of northern Arizona, and the Pacific Northwest. The team has its headquarters in Menlo Park, California, and maintains smaller field offices at several other locations in the western United States. This compilation gives the bibliographical citations for 123 new publications, most of which are available online using the hyperlinks provided.

Upcoming satellite constellations will substantially increase the amount of Earth Observation (EO) data, and presents us with the challenge of consistently using all these available information to infer the state of the landsurface, parameterised through Essential Climate Variables (ECVs). A promising approach to this problem is the use of physically based models that describe the processes that generate the images, using e.g. radiative transfer (RT) theory. However, these models need to be inverted to infer the landsurface parameters from the observations, and there is often not enough information in the EO data to satisfactorily achieve this. Data assimilation (DA) approaches supplement the EO data with prior information in the form of models or prior parameter distributions, and have the potential for solving the inversion problem. These methods however are computationally expensive. In this study, we show the use of fast surrogate models of the RT codes (emulators) based on Gaussian Processes (Gomez-Dans et al, 2016) embedded with the Earth Observation Land Data Assimilation System (EO-LDAS) framework (Lewis et al 2012) in order to estimate the surface of the landsurface from a heterogeneous set of optical observations. The study uses time series of moderate spatial resolution observations from MODIS (250 m), MERIS (300 m) and MISR (275 m) over one site to infer the temporal evolution of a number of landsurface parameters (and associated uncertainties) related to vegetation: leaf area index (LAI), leaf chlorophyll content, etc. These parameter estimates are then used as input to an RT model (semidiscrete or PROSAIL, for example) to calculate fluxes such as broad band albedo or fAPAR. The study demonstrates that blending different sensors in a consistent way using physical models results in a rich and coherent set of landsurface parameters retrieved, with quantified uncertainties. The use of RT models also allows for the consistent prediction of fluxes

Land cover is an important variable for many studies involving the Earthsurface, such as climate, food security, hydrology, soil erosion, atmospheric quality, conservation biology, and plant functioning. Land cover not only changes with human caused land use changes, but also changes with nature. Therefore, the state of land cover is highly dynamic. In winter snow shields underneath various other land cover types in higher latitudes. Floods may persist for a long period in a year over low land areas in the tropical and subtropical regions. Forest maybe burnt or clear cut in a few days and changes to bare land. Within several months, the coverage of crops may vary from bare land to nearly 100% crops and then back to bare land following harvest. The highly dynamic nature of land cover creates a challenge in mapping and monitoring which remains to be adequately addressed. As economic globalization continues to intensify, there is an increasing trend of land cover/land use change, environmental pollution, land degradation, biodiversity loss at the global scale, timely and reliable information on global land cover and its changes is urgently needed to mitigate the negative impact of global environment change.

This textbook describes some of the most effective and straightforward quantitative techniques for modeling Earthsurface processes. By emphasizing a core set of equations and solution techniques, the book presents state-of-the-art models currently employed in Earthsurface process research, as well as a set of simple but practical research tools. Detailed case studies demonstrate application of the methods to a wide variety of processes including hillslope, fluvial, aeolian, glacial, tectonic, and climatic systems. Exercises at the end of each chapter begin with simple calculations and then progress to more sophisticated problems that require computer programming. All the necessary computer codes are available online at www.cambridge.org/9780521855976. Assuming some knowledge of calculus and basic programming experience, this quantitative textbook is designed for advanced geomorphology courses and as a reference book for professional researchers in Earth and planetary science looking for a quantitative approach to Earthsurface processes.

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's landsurface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of landsurface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Boeing has been developing design for a set of small landing PODS that could be deployed from a spacecraft bus orbiting a NEA to address the set of SKGs for investigation prior to crewed missions to Near Earth Asteroids or the moons of Mars.

Information on LandSurface Temperature (LST) can be generated from observations made from satellites in low Earth orbit (LEO) such as MODIS and ASTER and by sensors in geostationary Earth orbit (GEO) such as GOES. Under a project titled: "A Unified and Coherent LandSurface Temperature and Emissivity Earth System Data Record for Earth Science" led by Jet Propulsion Laboratory, an effort is underway to develop long term consistent information from both such systems. In this presentation we will describe an effort to derive LST information from GOES satellites. Results will be presented from two approaches: 1) based on regression developed from a wide range of simulations using MODTRAN, SeeBor Version 5.0 global atmospheric profiles and the CAMEL (Combined ASTER and MODIS Emissivity for Land) product based on the standard University of Wisconsin 5 km emissivity values (UWIREMIS) and the ASTER Global Emissivity Database (GED) product; 2) RTTOV radiative transfer model driven with MERRA-2 reanalysis fields. We will present results of evaluation of these two methods against various products, such as MOD11, and ground observations for the five year period of (2004-2008).

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth’s landsurface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of landsurface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. PMID:28953943

From 1915 to 1975, more than 109 million acre-feet of ground water was withdrawn from about 4,500 square miles in Pinal and Maricopa Counties in south-central Arizona. The volume of water withdrawn greatly exceeds the volume of natural recharge, and water levels have been declining since 1923. As a result of the water-level declines, the landsurface has subsided, the alluvial deposits have been subjected to stress, and earth fissures have developed. Land subsidence and earth fissures have damaged public and private properties. Subsidence and fissures will continue to occur as long as ground water is being mined and water levels continue to decline. As urban development expands, land subsidence and earth fissures will have an increasing socioeconomic impact. Information on maps includes change in water levels, measurements of land subsidence, and location of earth fissures. A section showing land subsidence between Casa Grande and the Picacho Peak Interchange also is included. Scale 1:250,000. (Woodard-USGS)

This Virginia coal company's surface mined lands show an adequate stocking of tree seedlings in terms of number per acre, but the distribution of seedlings has been affected by past reclamation practices. Natural reseeding has been an important contributor to the present seedling stock.

The Advanced Land Imager (ALI) is one of three instruments to be flown on the first Earth Observing mission (EO-1) under NASA's New Millennium Program (NMP). ALI contains a number of innovative features, including a wide field of view optical design, compact multispectral focal plane arrays, non-cryogenic HgCdTe detectors for the short wave infrared bands, and silicon carbide optics. This document outlines the techniques adopted during ground calibration of the radiometric response of the Advanced Land Imager. Results from system level measurements of the instrument response, signal-to-noise ratio, saturation radiance, and dynamic range for all detectors of every spectral band are also presented.

A topic of study becomes a new field when it provides a useful conceptual framework for understanding suites of important processes. Geobiology integrates microbial biology with Earth sciences in a way that allows us to ask - and answer - deeper questions about Earth and the life on it. Recent studies of the oxidation of Earth's surface exemplify the impact of Geobiology as a new field. For decades, scientists have understood that Earth's surface was oxidized by photosynthesis. Geochemical records indicate dramatic redox changes both globally, e.g. the loss of MIF sulfur signatures due to formation of an ozone layer, and locally, as preserved in sedimentary rocks. However, these records depend critically on the dynamics of both the global biosphere and local microbial ecology. For example, an increase in global redox due to photosynthetic iron oxidation has different biogeochemical implications than an increase from oxygenic photosynthesis; O2 reacts very differently with organic matter and minerals than iron oxyhydroxides do, influencing microbial ecology as well as potential geochemical signatures in sedimentary rocks. Thus, studies of modern microbial communities provide insights into the interactions among metabolisms and geochemical gradients that have shaped Earth's redox history. For example, the ability of cyanobacteria to create O2 oases in benthic mats and soils on land provides a new framework for evaluating redox-sensitive elemental fluxes to the ocean. Similarly, genomic studies of Cyanobacteria have revealed close relatives, Melainabacteria, that are mostly obligate anaerobes. The evolutionary relationships between these two groups, as preserved in their genomes, reflect important microbial processes that led to oxidation of Earth's surface. By combining insights from microbial biology and sedimentary geochemistry, geobiologists will develop significantly more accurate models of the interactions between life and Earth.

The increasing availability of high-quality optical satellite images should allow, in principle, continuous monitoring of Earth's surface changes due to geologic processes, climate change, or anthropic activity. For instance, sequential optical images have been used to measure displacements at Earth's surface due to coseismic ground deformation [e.g., Van Puymbroeck et al., 2000], ice flow [Scambos et al., 1992; Berthier et al., 2005], sand dune migration [Crippen, 1992], and landslides [Kääb, 2002; Delacourt et al., 2004]. Surface changes related to agriculture, deforestation, urbanization, and erosion-which do not involve ground displacement-might also be monitored, provided that the images can be registered with sufficient accuracy. Although the approach is simple in principle, its use is still limited, mainly because of geometric distortion of the images induced by the imaging system, biased correlation techniques, and implementation difficulties.

Landsurface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the landsurface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth"s water cycle and climate variability. NASA"s Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type).

What kinds of patterns will characterize Earth's surface in 200 years? This question is addressed using a complex systems dynamical framework for distinct levels of description in a hierarchy, in which time scale and spatial extent increase and number of variables decrease with level, and in which levels are connected nonlinearly to each other via self-organization and slaving and linearly to the external environment. Self-organized patterns linking the present to 200 years in the future must be described dynamically on a level with a time scale of centuries. Human-landscape coupling will play a prominent role in the formation of these patterns as population peaks and interactions become nonlinear over these time scales. Three related examples illustrate this approach. First, the response of human-occupied coastlines to rising sea level. Coastlines in wealthy regions develop a spatially varying boom and bust pattern, with response amplified by structures meant to delay the effects of sea level rise. Coastlines in economically disadvantaged regions experience a subdued response, with populations developing a culture of displacement that minimizes human-landscape interactions in a context of scarce resources. Second, the evolution of nation-state borders with degrading ecosystems, declining resource availability and increasing transportation costs. The maintenance of strong borders as selective filtration systems (goods, capital and people) is based on a cost-benefit analysis in which the economic benefits accruing from long distance, globalized resource exploitation are weighed against policing and infrastructure costs. As costs rise above benefits, borders fragment, with a transition to local barriers and conflicts, and mobile peoples moving to resources. Third, trends in urbanization and development of megacities under economic and environmental stress. The pattern of rapid growth of megacities through inward migration, with displaced people occupying high

Landsurface models (LSMs) play a critical role in the simulation of climate, for they determine the character of a large fraction of the atmosphere's lower boundary. The LSM partitions the net radiative energy at the landsurface into sensible heat, latent heat, and energy storage, and it partitions incident precipitation water into evaporation, runoff, and water storage. Numerous modeling experiments and the existing (though very scant) observational evidence suggest that variations in these partitionings can feed back on the atmospheric processes that induce them. This land-atmosphere feedback can in turn have a significant impact on the generation of continental precipitation. For this and other reasons (including the role of the landsurface in converting various atmospheric quantities, such as precipitation, into quantities of perhaps higher societal relevance, such as runoff), many modeling groups are placing a high emphasis on improving the treatment of landsurface processes in their models. LSMs have evolved substantially from the original bucket model of Manabe et al. This evolution, which is still ongoing, has been documented considerably. The present paper also takes a look at the evolution of LSMs. The perspective here, though, is different - the evolution is considered strictly in terms of the 'balance' between the formulations of evaporation and runoff processes. The paper will argue that a proper balance is currently missing, largely due to difficulties in treating subgrid variability in soil moisture and its impact on the generation of runoff.

One of the consequences of wildfires is the changes in land-surface properties such as removal of vegetation. This will change local and regional climate through modifying the land-air heat and water fluxes. This study investigates mechanism by developing and a parameterization of fire-induced land-surface property changes and applying it to modeling of the climate impacts of large wildfires in the United States. Satellite remote sensing was used to quantitatively evaluate the land-surface changes from large fires provided from the Monitoring Trends in Burning Severity (MTBS) dataset. It was found that the changes in land-surface properties induced by fires are very complex, depending on vegetation type and coverage, climate type, season and time after fires. The changes in LAI are remarkable only if the actual values meet a threshold. Large albedo changes occur in winter for fires in cool climate regions. The signs are opposite between the first post-fire year and the following years. Summer day-time temperature increases after fires, while nigh-time temperature changes in various patterns. The changes are larger in forested lands than shrub / grassland lands. In the parameterization scheme, the detected post-fire changes are decomposed into trends using natural exponential functions and fluctuations of periodic variations with the amplitudes also determined by natural exponential functions. The final algorithm is a combination of the trends, periods, and amplitude functions. This scheme is used with Earth system models to simulate the local and regional climate effects of wildfires.

Collaboration between the integrated assessment modeling (IAM) and earth system modeling (ESM) communities is increasing, driven by a growing interest in research questions that require analysis integrating both social and natural science components. This collaboration often takes the form of integrating their respective models. There are a number of approaches available to implement this integration, ranging from one-way linkages to full two-way coupling, as well as approaches that retain a single modeling framework but improve the representation of processes from the other framework. We discuss the pros and cons of these different approaches and the conditions under which a two-way coupling of IAMs and ESMs would be favored over a one-way linkage. We propose a criterion that is necessary and sufficient to motivate two-way coupling: A human process must have an effect on an earth system process that is large enough to cause a change in the original human process that is substantial compared to other uncertainties in the problem being investigated. We then illustrate a test of this criterion for land use-climate interactions based on work using the Community Earth System Model (CESM) and land use scenarios from the Representative Concentration Pathways (RCPs), in which we find that the land use effect on regional climate is unlikely to meet the criterion. We then show an example of implementing a one-way linkage of land use and agriculture between an IAM, the integrated Population-Economy-Technology-Science (iPETS) model, and CESM that produces fully consistent outcomes between iPETS and the CESM landsurface model. We use the linked system to model the influence of climate change on crop yields, agricultural land use, crop prices and food consumption under two alternative future climate scenarios. This application demonstrates the ability to link an IAM to a global landsurface and climate model in a computationally efficient manner.

Recent studies indicate that changes in surface climate and carbon fluxes caused by land management (i.e., modifications of vegetation structure without changing the type of land cover) can be as large as those caused by land cover change. Further, such effects may occur on substantial areas: while about one quarter of the landsurface has undergone land cover change, another fifty percent are managed. This calls for integration of management processes in Earth system models (ESMs). This integration increases the importance of awareness and agreement on how to diagnose effects of land use in ESMs to avoid additional model spread and thus unnecessary uncertainties in carbon budget estimates. Process understanding of management effects, their model implementation, as well as data availability on management type and extent pose challenges. In this respect, a significant step forward has been done in the framework of the current IPCC's CMIP5 simulations (Coupled Model Intercomparison Project Phase 5): The climate simulations were driven with the same harmonized land use dataset that, different from most datasets commonly used before, included information on two important types of management: wood harvest and shifting cultivation. However, these new aspects were employed by only part of the CMIP5 models, while most models continued to use the associated land cover maps. Here, we explore the consequences for the carbon cycle of including subgrid-scale land transformations ("gross transitions"), such as shifting cultivation, as example of the current state of implementation of land management in ESMs. Accounting for gross transitions is expected to increase land use emissions because it represents simultaneous clearing and regrowth of natural vegetation in different parts of the grid cell, reducing standing carbon stocks. This process cannot be captured by prescribing land cover maps ("net transitions"). Using the MPI-ESM we find that ignoring gross transitions

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year, the last of the first series of EOS missions, Aura, was launched. Aura is designed exclusively to conduct research on the composition, chemistry, and dynamics of the Earth's upper and lower atmosphere, employing multiple instruments on a single spacecraft. Aura is the third in a series of major Earth observing satellites to study the environment and climate change and is part of NASA's Earth Science Enterprise. The first and second missions, Terra and Aqua, are designed to study the land, oceans, atmospheric constituents (aerosols, clouds, temperature, and water vapor), and the Earth's radiation budget. The other seven EOS spacecraft include satellites to study (i) land cover & land use change, (ii) solar irradiance and solar spectral variation, (iii) ice volume, (iv) ocean processes (vector wind and sea surface topography), and (v) vertical variations of clouds, water vapor, and aerosols up to and including the stratosphere. Aura's chemistry measurements will also follow up on measurements that began with NASA's Upper Atmosphere Research Satellite and continue the record of satellite ozone data collected from the TOMS missions. In this presentation I will describe how scientists are using EOS data to examine the health of the earth's atmosphere, including atmospheric chemistry, aerosol properties, and cloud properties, with a special but not exclusive look at the latest earth observing mission, Aura.

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by whch scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. During this year, the last of the first series of EOS missions, Aura, was launched. Aura is designed exclusively to conduct research on the composition, chemistry, and dynamics of the Earth's upper and lower atmosphere, employing multiple instruments on a single spacecraft. Aura is the third in a series of major Earth observing satellites to study the environment and climate change and is part of NASA's Earth Science Enterprise. The first and second missions, Terra and Aqua, are designed to study the land, oceans, atmospheric constituents (aerosols, clouds, temperature, and water vapor), and the Earth's radiation budget. The other seven EOS spacecraft include satellites to study (i) land cover & land use change, (ii) solar irradiance and solar spectral variation, (iii) ice volume, (iv) ocean processes (vector wind and sea surface topography), and (v) vertical variations of clouds, water vapor, and aerosols up to and including the stratosphere. Aura's chemistry measurements will also follow up on measurements that began with NASA's Upper Atmosphere Research Satellite and continue the record of satellite ozone data collected from the TOMS missions. In this presentation I will describe how scientists are using EOS data to examine the health of the earth's atmosphere, including atmospheric chemistry, aerosol properties, and cloud properties, with a special look at the latest earth observing mission, Aura.

Landsurface models have been used to assess water resources sustainability under changing Earth environment and increasing human water needs. Overwhelming observational records indicate that human activities have ubiquitous and pertinent effects on the hydrologic cycle; however, they have been crudely represented in large scale landsurface models. In this study, we enhance an integrated continental-scale land hydrology model named Leaf-Hydro-Flood to better represent land-water management. The model is implemented at high resolution (5km grids) over the continental US. Surface water and groundwater are withdrawn based on actual practices. Newly added irrigation, water diversion, and dam operation schemes allow better simulations of stream flows, evapotranspiration, and infiltration. Results of various hydrologic fluxes and stores from two sets of simulation (one with and the other without human activities) are compared over a range of river basin and aquifer scales. The improved simulations of land hydrology have potential to build consistent modeling framework for human-water-climate interactions.

Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of landsurface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

The paper considers modelling of the surface when fixing objects in the geocentric coordinate systems in the course of GLONASS satellite system development. The authors revealed new approaches to presentation of geographical data to a user, transformation of map properties and the leading role of ERS (Earth remote sensing) as a source of mapping information; change of scientific paradigms aimed at improvement of high-accuracy cartographic objects representation in the plane.

A scientist's gloved hand holds one of the numerous rock samples brought back to Earth from the Apollo 12 lunar landing mission. This sample is a highly shattered basaltic rock with a thin black-glass coating on five of its six sides. Glass fills fractures and cements the rock together. The rock appears to have been shattered and thrown out by a meteorite impact explosion and coated with molten rock material before the rock fell to the surface.

We briefly review current knowledge and pinpoint some of the major areas of uncertainty for the following fundamental processes: (1) convection, condensation nuclei, and cloud formation; (2) oceanic circulation and its coupling to the atmosphere and cryosphere; (3) landsurface hydrology and hydrology-vegetation coupling; (4) biogeochemistry of greenhouse gases; and (5) upper atmospheric chemistry and circulation.

Many in the science community want a Mars sample return in the near future, with the expectation that it will provide in-depth information, significantly beyond what we know from remote sensing, limited in-situ measurements, and work with Martian meteorites. Certainly, return of samples from the Moon resulted in major advances in our understanding of both the geologic history of our planetary satellite, and its relationship to Earth. Similar scientific insights would be expected from analyses of samples returned from Mars. Unfortunately, Mars-lander sample-return missions have been delayed, for the reason that NASA needs more time to review the complexities and risks associated with that type of mission. A traditional sample return entails a complex transfer-chain, including landing, collection, launch, rendezvous, and the return to Earth, as well as an evaluation of potential biological hazards involved with bringing pristine Martian organics to Earth. There are, however, means of returning scientifically-rich samples from Mars without landing on the surface. This paper discusses an approach for returning intact samples of surface dust, based on known instrument technology, without using an actual Martian lander.

The LSI community of users is large and varied. To reach all these users as well as potential instrument contributors this document has been organized by measurement parameters of interest such as Leaf Area Index and LandSurface Temperature. These measurement parameters and the data presented in this document are drawn from multiple sources, listed at the end of the document, although the two primary ones are "The Space-Based Global Observing System in 2010 (GOS-2010)" that was compiled for the World Meteorological Organization (WMO) by Bizzarro Bizzarri, and the CEOS Missions, Instruments, and Measurements online database (CEOS MIM). For each measurement parameter the following topics will be discussed: (1) measurement description, (2) applications, (3) measurement spectral bands, and (4) example instruments and mission information. The description of each measurement parameter starts with a definition and includes a graphic displaying the relationships to four general landsurface imaging user communities: vegetation, water, earth, and geo-hazards, since the LSI community of users is large and varied. The vegetation community uses LSI data to assess factors related to topics such as agriculture, forest management, crop type, chlorophyll, vegetation land cover, and leaf or canopy differences. The water community analyzes snow and lake cover, water properties such as clarity, and body of water delineation. The earth community focuses on minerals, soils, and sediments. The geo-hazards community is designed to address and aid in emergencies such as volcanic eruptions, forest fires, and large-scale damaging weather-related events.

Landsurface temperature (LST) is a key surface boundary condition that is significantly correlated to surface flux partitioning between latent and sensible heat. The spatial and temporal variation in LST is driven by radiation, wind, vegetation cover and roughness as well as soil moisture status in the surface and root zone. Data from airborne and satellite-based platforms provide LST from ~10 km to sub meter resolutions. A landsurface scheme called the Two-Source Energy Balance (TSEB) model has been incorporated into a multi-scale regional modeling system ALEXI (Atmosphere Land Exchange Inverse) and a disaggregation scheme (DisALEXI) using higher resolution LST. Results with this modeling system indicates that it can be applied over heterogeneous landsurfaces and estimate reliable surface fluxes with minimal in situ information. Consequently, this modeling system allows for scaling energy fluxes from subfield to regional scales in regions with little ground data. In addition, the TSEB scheme has been incorporated into a large Eddy Simulation (LES) model for investigating dynamic interactions between variations in the landsurface state reflected in the spatial pattern in LST and the lower atmospheric air properties affecting energy exchange. An overview of research results on scaling of fluxes and interactions with the lower atmosphere from the subfield level to regional scales using the TSEB, ALEX/DisALEX and the LES-TSEB approaches will be presented. Some unresolved issues in the use of LST at different spatial resolutions for estimating surface energy balance and upscaling fluxes, particularly evapotranspiration, will be discussed.

Interaction of groundwater with the landsurface impacts a wide range of climatic, hydrologic, ecologic and geomorphologic processes. Many site-specific studies have successfully focused on measuring and modelling groundwater-surface water interaction, but upscaling or estimation at catchment or regional scale appears to be challenging. The factors controlling the interaction at regional scale are still poorly understood. In this contribution, a new 2-D (cross-sectional) analytical groundwater flow solution is used to derive a dimensionless criterion that expresses the conditions under which the groundwater outcrops at the landsurface (Bresciani et al., 2016). The criterion gives insights into the functional relationships between geology, topography, climate and the locations of groundwater discharge along river systems. This sheds light on the debate about the topographic control of groundwater flow and groundwater-surface water interaction, as effectively the topography only influences the interaction when the groundwater table reaches the landsurface. The criterion provides a practical tool to predict locations of groundwater discharge if a limited number of geomorphological and hydrogeological parameters (recharge, hydraulic conductivity and depth to impervious base) are known, and conversely it can provide regional estimates of the ratio of recharge over hydraulic conductivity if locations of groundwater discharge are known. A case study with known groundwater discharge locations located in South-West Brittany, France shows the feasibility of regional estimates of the ratio of recharge over hydraulic conductivity. Bresciani, E., Goderniaux, P. and Batelaan, O., 2016, Hydrogeological controls of water table-landsurface interactions. Geophysical Research Letters 43(18): 9653-9661. http://dx.doi.org/10.1002/2016GL070618

The next generation of weather and climate models permits convection, albeit at a grid spacing that is not sufficient to resolve all details of the clouds. Whereas much attention is being devoted to the correct simulation of convective clouds and associated precipitation, the role of the landsurface has received far less interest. In our view, convective permitting simulations pose a set of problems that need to be solved before accurate weather and climate prediction is possible. The heart of the problem lies at the direct runoff and at the nonlinearity of the surface stress as a function of soil moisture. In coarse resolution simulations, where convection is not permitted, precipitation that reaches the landsurface is uniformly distributed over the grid cell. Subsequently, a fraction of this precipitation is intercepted by vegetation or leaves the grid cell via direct runoff, whereas the remainder infiltrates into the soil. As soon as we move to convection permitting simulations, this precipitation falls often locally in large amounts. If the same land-surface model is used as in simulations with parameterized convection, this leads to an increase in direct runoff. Furthermore, spatially non-uniform infiltration leads to a very different surface stress, when scaled up to the course resolution of simulations without convection. Based on large-eddy simulation of realistic convection events at a large domain, this study presents a quantification of the errors made at the landsurface in convection permitting simulation. It compares the magnitude of the errors to those made in the convection itself due to the coarse resolution of the simulation. We find that, convection permitting simulations have less evaporation than simulations with parameterized convection, resulting in a non-realistic drying of the atmosphere. We present solutions to resolve this problem.

We propose an Earth-observation mission Land Observation from Geosynchronous Earth Orbit (LOGEO) to place two spin-scan-stabilized 500-m resolution 9-band VNIR-SWIR imagers in a near-geosynchronous inclined orbit, allowing 15 min observations with a full range of daily sun angles and 30∘ variations in view angle. LOGEO drifts westward at about 4∘ per day, providing geostationary-style coverage for all points on the globe eight times per year. This unique imaging geometry allows accurate retrievals of daily changes in surface bidirectional reflectance, which in turn enhances direct retrieval of biophysical properties, as well as long term and consistent landsurface parameters for modeling studies that seek to understand the Earth system and its interactions. For studies of climate and environmental dynamics, LOGEO provides accurate observations of atmospheric aerosols, clouds, as well as other atmospheric constituents across a diverse number of spatial and temporal scales. This collection of land, atmospheric, and climate data products are directly applicable to seven of the nine GEOSS societal benefits areas, providing great opportunities for international collaboration. We also present an overview of LOGEO's systems architecture, as well as top-level design-trade studies and orbital scenarios.

satellite derived Landsurface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. However, the accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. A new comprehensive and systemic project of china, called the Remote Sensing Application Network (CRSAN), has been launched recent years. Two subjects of this project is developing a Multi-source data Synergized Quantitative Remote Sensin g Production System ( MuSyQ ) and a Web-based validation system named LAndsurface remote sensing Product VAlidation System (LAPVAS) , which aims to generate a quantitative remote sensing product for ecosystem and environmental monitoring and validate them with a reference validation data and a standard validation system, respectively. Landsurface BRDF/albedo is one of product datasets of MuSyQ which has a pentad period with 1km spatial resolution and is derived by Multi-sensor Combined BRDF Inversion ( MCBI ) Model. In this MuSyQ albedo evaluation, a multi-validation strategy is implemented by LAPVAS, including directly and multi-scale validation with field measured albedo and cross validation with MODIS albedo product with different land cover. The results reveal that MuSyQ albedo data with a 5-day temporal resolution is in higher sensibility and accuracy during land cover change period, e.g. snowing. But results without regard to snow or changed land cover, MuSyQ albedo generally is in similar accuracy with MODIS albedo and meet the climate modeling requirement of an absolute accuracy of 0.05.

The scientific requirements for mapping the global landsurface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global landsurface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

Anthropogenic modification of the earth's surface is discussed in two problem areas: (1) land use changes and overgrazing, and how it affects albedo and landsurface-atmosphere interactions, and (2) water and landsurface pollution, especially oil slicks. A literature survey evidences the importance of these problems. The need for monitoring is stressed, and it is suggested that with some modifications to the sensors, ERTS (Landsat) series satellites can provide approximate monitoring information. The European Landsat receiving station in Italy will facilitate data collection for the tasks described.

Presented here is the landsurface IR spectral emissivity retrieved from the Cross-track Infrared Sounder (CrIS) measurements. The CrIS is aboard the Suomi National Polar-orbiting Partnership (NPP) satellite launched on October 28, 2011. We describe the retrieval algorithm, demonstrate the surface emissivity retrieved with CrIS measurements, and inter-comparison with the Infrared Atmospheric Sounding Interferometer (IASI) emissivity. We also demonstrate that surface emissivity from satellite measurements can be used in assistance of monitoring global surface climate change, as a long-term measurement of IASI and CrIS will be provided by the series of EUMETSAT MetOp and US Joint Polar Satellite System (JPSS) satellites. Monthly mean surface properties are produced using last 5-year IASI measurements. A temporal variation indicates seasonal diversity and El Nino/La Nina effects not only shown on the water but also on the land. Surface spectral emissivity and skin temperature from current and future operational satellites can be utilized as a means of long-term monitoring of the Earth's environment. CrIS spectral emissivity are retrieved and compared with IASI. The difference is small and could be within expected retrieval error; however it is under investigation.

One of the crucial tasks for climatic and hydrological scientists over the next several years will be validating landsurface process parameterizations used in climate models. There is not, necessarily, a unique set of parameters to be used. Different scientists will want to attempt to capture processes through various methods “for example, Avissar and Verstraete, 1990”. Validation of some aspects of the available (and proposed) schemes' performance is clearly required. It would also be valuable to compare the behavior of the existing schemes [for example, Dickinson et al., 1991; Henderson-Sellers, 1992a].The WMO-CAS Working Group on Numerical Experimentation (WGNE) and the Science Panel of the GEWEX Continental-Scale International Project (GCIP) [for example, Chahine, 1992] have agreed to launch the joint WGNE/GCIP Project for Intercomparison of Land-Surface Parameterization Schemes (PILPS). The principal goal of this project is to achieve greater understanding of the capabilities and potential applications of existing and new land-surface schemes in atmospheric models. It is not anticipated that a single “best” scheme will emerge. Rather, the aim is to explore alternative models in ways compatible with their authors' or exploiters' goals and to increase understanding of the characteristics of these models in the scientific community.

Monitoring LandSurface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

Accurate temporal and spatial estimation of landsurface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfo...

Envisat's Medium Resolution Imaging Spectrometer (MERIS) acquires multi-spectral imagery of the Earth in the optical domain over terrestrial surfaces since a decade at global scale. Characteristics of the instrument will be first summarized and then several achievements for terrestrial applications will be presented as scientists have used data or products for characterizing the state of the global system and its variability. The ESA- GlobCover which has developed a service capable of delivering land cover maps on the basis of 300m spectral reflectance measurements will be summarized (Bicheron et al, 2008; Arino et al, 2010). We will highlight recent research results for continental land cover mapping over South America using MERIS data from 2009 and 2010 (Hojas Gascon et al, 2012). The objective and originality of this work was to generate a new land cover using selected MERIS data instead of all available images and supplementary information from associated Level-2 product, i.e. FAPAR, provided at a reduced resolution of 1 km. As the actual two level-2 products provided by ESA correspond to geophysical products such as the FAPAR (Fraction of Synthetically Active Radiation) and the MERIS terrestrial chlorophyll index (MTCI). With this latter, phenology studies as this product is sensitive to canopy chlorophyll content, a vegetation property that is a useful proxy for the canopy physical and chemical alterations associated with phenological change. (Dash and Curran, 2004; Boyd et al, 2011). For exploiting the use of the MGVI, ESA-Joint Research Center (JRC) FAPAR project has been created for producing time-series of geophysical products and will be summarized (Gobron et al, 2006, 2010; Fusco et al, 2003). It uses through various examples such as assessing drought at regional scale (Rossi etl a, 2010) or monitoring the state and changes of landsurfaces global scale (Gobron et al, 2012). As the FAPAR value constitutes a state variable in advanced Earth system models

The method is illustrated using a combination of MODIS NDVI data with a spatial resolution of 250m and 3 Km Meteosat Second Generation SEVIRI LST data. Geostationary Earth Observation data carry a large potential for assessment of surface state variables. Not the least the European Meteosat Second Generation platform with its SEVIRI sensor is well suited for studies of the dynamics of landsurfaces due to its high temporal frequency (15 minutes) and its red, Near Infrared (NIR) channels that provides vegetation indices, and its two split window channels in the thermal infrared for assessment of LandSurface Temperature (LST). For some applications the spatial resolution in geostationary data is too coarse. Due to the low statial resolution of 4.8 km at nadir for the SEVIRI sensor, a means of providing sub pixel information is sought for. By combining and properly scaling two types of satellite images, namely data from the MODIS sensor onboard the polar orbiting platforms TERRA and AQUA and the coarse resolution MSG-SEVIRI, we exploit the best from two worlds. The vegetation index/surface temperature space has been used in a vast number of studies for assessment of air temperature, soil moisture, dryness indices, evapotranspiration and for studies of land use change. In this paper, we present an improved method to derive a finer resolution LandSurface Temperature (LST). A new, deterministic scaling method has been applied, and is compared to existing deterministic downscaling methods based on LST and NDVI. We also compare our results from in situ measurements of LST from the Dahra test site in West Africa.

The topics covered include the following: prototype land cover characteristics data base for the conterminous United States; surface evapotranspiration effects on cumulus convection and implications for mesoscale models; the use of complex treatment of surface hydrology and thermodynamics within a mesoscale model and some related issues; initialization of soil-water content for regional-scale atmospheric prediction models; impact of surface properties on dryline and MCS evolution; a numerical simulation of heavy precipitation over the complex topography of California; representing mesoscale fluxes induced by landscape discontinuities in global climate models; emphasizing the role of subgrid-scale heterogeneity in surface-air interaction; and problems with modeling and measuring biosphere-atmosphere exchanges of energy, water, and carbon on large scales.

Glass and glass ceramic samples exposed to the low earth orbit environment for approximately 5.5 years on the Long Duration Exposure Facility (LDEF) were found to display limited degradation in optical transmission. Commercial optical quality fused silica samples display decreases in transmission in the 200 to 400 nm wavelength region, and this degradation appears to be a consequence of surface contamination. The contamination, found only on internal surfaces of samples, was measured by medium energy backscattering spectrometry and found to be primarily carbon. Additional thin film contamination by a species with atomic mass near 64, which was present at the level of about 8 x 10 exp 14/sq. cm has not been identified. These observations are consistent with the interpretation that organic binders used in the black absorbing paint (Chem Glaze Z-306) inside the sample holding tray were concentrated in the vicinity of the samples and photolytically cracked by solar UV radiation. The resulting decomposition products were deposited on the interior sample surface and gave rise to the optical transmission loss. No detectable contamination was observed on the external or space exposed surface of the samples. No measurable damage was detected which could be attributed to the direct action of gamma or UV radiation on the glass samples. These results emphasize the need for special precautions in the preparation of spacecraft carrying precision optical components on long duration missions.

The panel defined three main areas of study that are central to the Solid Earth Science (SES) program: climate interactions with the Earth's surface, tectonism as it affects the Earth's surface and climate, and human activities that modify the Earth's surface. Four foci of research are envisioned: process studies with an emphasis on modern processes in transitional areas; integrated studies with an emphasis on long term continental climate change; climate-tectonic interactions; and studies of human activities that modify the Earth's surface, with an emphasis on soil degradation. The panel concluded that there is a clear requirement for global coverage by high resolution stereoscopic images and a pressing need for global topographic data in support of studies of the landsurface.

Orbital and landing operations about near-Earth asteroids are different than classical orbital operations about large bodies. The major differences lie with the small mass of the asteroid, the lower orbital velocities, the larger Solar tide and radiation pressure perturbations, the irregular shape of the asteroid and the potential for non-uniform rotation of the asteroid. These differences change the nature of orbits about an asteroid to where it is often common to find trajectories that evolve from stable, near-circular orbits to crashing or escaping orbits in a matter of days. The understanding and control of such orbits is important if a human or robotic presence at asteroids is to be commonplace in the future.

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for landsurface model evaluation called the Landsurface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the landsurface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the landsurface modeling community.

Changes in land use, land cover, or both promote changes in surface temperature that can amplify or dampen long-term trends driven by natural and anthropogenic climate change by modifying the surface energy budget, primarily through differences in albedo, evapotranspiration, and aerodynamic roughness. Recent advances in variable resolution global models provide the tools necessary to investigate local and global impacts of land use and land cover change by embedding a high-resolution grid over areas of interest in a seamless and computationally efficient manner. Here, we used two eddy covariance tower clusters in the Eastern US (University of New Hampshire UNH and Duke Forest) to validate simulation of surface energy fluxes and properties by the uncoupled Community Land Model (PTCLM4.5) and coupled land-atmosphere Variable-Resolution Community Earth System Model (VR-CESM1.3). Surface energy fluxes and properties are generally well captured by the models for grassland sites, however forested sites tend to underestimate latent heat and overestimate sensible heat flux. Surface roughness emerged as the dominant biophysical forcing factor affecting surface temperature in the eastern United States, generally leading to warmer nighttime temperatures and cooler daytime temperatures. However, the sign and magnitude of the roughness effect on surface temperature was highly sensitive to the calculation of aerodynamic resistance to heat transfer.

The Committee on Earth Observation Satellites is an international group that coordinates civil space-borne observations of the Earth, and provides the space component of the Global Earth Observing System of Systems (GEOSS). The CEOS Virtual Constellations concept was implemented in an effort to engage and coordinate disparate Earth observing programs of CEOS member agencies and ultimately facilitate their contribution in supplying the space-based observations required to satisfy the requirements of the GEOSS. The CEOS initially established Study Teams for four prototype constellations that included precipitation, landsurface imaging, ocean surface topography, and atmospheric composition. The basic mission of the LandSurface Imaging (LSI) Constellation [1] is to promote the efficient, effective, and comprehensive collection, distribution, and application of space-acquired image data of the global landsurface, especially to meet societal needs of the global population, such as those addressed by the nine Group on Earth Observations (GEO) Societal Benefit Areas (SBAs) of agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather. The LSI Constellation Portal is the result of an effort to address important goals within the LSI Constellation mission and provide resources to assist in planning for future space missions that might further contribute to meeting those goals.

Soil and vegetation exert strong control over the evapotranspiration rate, which couples the landsurface water and energy balances. A method is presented to quantify the timescale of this surface control using daily general circulation model (GCM) simulation values of evapotranspiration and precipitation. By equating the time history of evaporation efficiency (ratio of actual to potential evapotranspiration) to the convolution of precipitation and a unit kernel (temporal weighting function), response functions are generated that can be used to characterize the timescales of evapotranspiration response for the landsurface model (LSM) component of GCMS. The technique is applied to the output of two multiyear simulations of a GCM, one using a Surface-Vegetation-Atmosphere-Transfer (SVAT) scheme and the other a Bucket LSM. The derived response functions show that the Bucket LSM's response is significantly slower than that of the SVAT across the globe. The analysis also shows how the timescales of interception reservoir evaporation, bare soil evaporation, and vegetation transpiration differ within the SVAT LSM.

Land-use change integrates a large number of components of the human and Earth systems, including climate, energy, water, and land. These complex coupling elements, interactions and feedbacks take place on a variety of space and time scales, thus increasing the complexity of land-use change modeling frameworks. In this study, we aim to identify which coupling elements, interactions and feedbacks are important for modeling land-use change, both at the global and regional level. First, we review the existing land-use change modeling framework used to develop land-use change projections for the Representative Concentration Pathways (RCP) scenarios. In such framework, land-use change is simulated by Integrated Assessment Models (IAMs) and mainly influenced by economic, energy, demographic and policy drivers. IAMs focus on representing the demand for agriculture and forestry goods (crops for food and bioenergy, forest products for construction and bioenergy), the interactions with other sectors of the economy and trade between various regions of the world. Then, we investigate how important various coupling elements and feedbacks with the Earth system are for projections of land-use change at the global and regional level. We focus on the following: i) the climate impacts on land productivity and greenhouse gas emissions, which requires climate change information and coupling to a terrestrial ecosystem model/crop model; ii) the climate and economic impacts on irrigation availability, which requires coupling the LUC modeling framework to a water resources management model and disaggregating rainfed and irrigated croplands; iii) the feedback of land-use change on the global and regional climate system through land-use change emissions and changes in the surface albedo and hydrology, which requires coupling to an Earth system model. Finally, we conclude our study by highlighting the current lack of clarity in how various components of the human and Earth systems are

The Western EarthSurfaces Processes Team (WESPT) of the U.S. Geological Survey, Geologic Division (USGS, GD), conducts geologic mapping and related topical earth- science studies in the western United States. This work is focused on areas where modern geologic maps and associated earth-science data are needed to address key societal and environmental issues such as ground-water quality, potential geologic hazards, and land-use decisions. Areas of primary emphasis currently include southern California, the San Francisco Bay region, and the Pacific Northwest. The team has its headquarters in Menlo Park, California, and maintains field offices at several other locations in the western United States. The results of research conducted by the WESPT are released to the public as a variety of databases, maps, text reports, and abstracts, both through the internal publication system of the USGS and in diverse external publications such as scientific journals and books. This report lists publications of the WESPT released in 1999 as well as additional 1997 and 1998 publications that were not included in the previous list (USGS Open-file Report 99-302). Most of the publications listed were authored or coauthored by WESPT staff. The list also includes some publications authored by non-USGS cooperators with the WESPT, as well as some authored by USGS staff outside the WESPT in cooperation with WESPT projects.

In MERRA-2, observed precipitation is inserted in place of model-generated precipitation at the landsurface. The use of observed precipitation was originally developed for MERRA-Land(a land-only replay of MERRA with model-generated precipitation replaced with observations).Previously shown that the land hydrology in MERRA-2 and MERRA-Land is better than MERRA. We test whether the improved landsurface hydrology in MERRA-2 leads to the expected improvements in the landsurface energy fluxes and 2 m air temperatures (T2m).

New Versions of MISR Aerosol and LandSurface Products Available Monday, February 12, ... the release of new versions of the MISR Level 2 (L2) Aerosol Product, the MISR L2 LandSurface Product, and the Level 3 (L3) Component Global Aerosol and LandSurface Products. The new MISR L2 Aerosol Product ...

The Western EarthSurface Processes Team (WESP) of the U.S. Geological Survey (USGS) conducts geologic mapping and related topical earth science studies in the western United States. This work is focused on areas where modern geologic maps and associated earth-science data are needed to address key societal and environmental issues such as ground-water quality, potential geologic hazards, and land-use decisions. Areas of primary emphasis in 2000 included southern California, the San Francisco Bay region, the Pacific Northwest, the Las Vegas urban corridor, and selected National Park lands. The team has its headquarters in Menlo Park, California, and maintains smaller field offices at several other locations in the western United States. The results of research conducted by the WESPT are released to the public as a variety of databases, maps, text reports, and abstracts, both through the internal publication system of the USGS and in diverse external publications such as scientific journals and books. This report lists publications of the WESPT released in 2000 as well as additional 1999 publications that were not included in the previous list (USGS Open-file Report 00-215). Most of the publications listed were authored or coauthored by WESPT staff. The list also includes some publications authored by non-USGS cooperators with the WESPT, as well as some authored by USGS staff outside the WESPT in cooperation with WESPT projects. Several of the publications listed are available on the World Wide Web; for these, URL addresses are provided. Many of these Web publications are USGS open-file reports that contain large digital databases of geologic map and related information.

Landsurface albedo is an important parameter in describing the radiative properties of the earth s surface as it represents the amount of incoming solar radiation that is reflected from the surface. The amount and type of vegetation of the surface dramatically alters the amount of radiation that is reflected; for example, croplands that contain leafy vegetation will reflect radiation very differently than blacktop associated with urban areas. In addition, since vegetation goes through a growth, or phenological, cycle, the amount of radiation that is reflected changes over the course of a year. As a result, albedo is both temporally and spatially dependant upon global location as there is a distribution of vegetated surface types and growing conditions. Landsurface albedo is critical for a wide variety of earth system research projects including but not restricted to remote sensing of atmospheric aerosol and cloud properties from space, ground-based analysis of aerosol optical properties from surface-based sun/sky radiometers, biophysically-based landsurface modeling of the exchange of energy, water, momentum, and carbon for various land use categories, and surface energy balance studies. These projects require proper representation of the surface albedo s spatial, spectral, and temporal variations, however, these representations are often lacking in datasets prior to the latest generation of landsurface albedo products.

One of the main objectives of the Sentinel-3 mission is to measure sea- and land-surface temperature with high-end accuracy and reliability in support of environmental and climate monitoring in an operational context. Calibration and validation are thus key criteria for operationalization within the framework of the Sentinel-3 Mission Performance Centre (S3MPC). Landsurface temperature (LST) has a long heritage of satellite observations which have facilitated our understanding of landsurface and climate change processes, such as desertification, urbanization, deforestation and land/atmosphere coupling. These observations have been acquired from a variety of satellite instruments on platforms in both low-earth orbit and in geostationary orbit. Retrieval accuracy can be a challenge though; surface emissivities can be highly variable owing to the heterogeneity of the land, and atmospheric effects caused by the presence of aerosols and by water vapour absorption can give a bias to the underlying LST. As such, a rigorous validation is critical in order to assess the quality of the data and the associated uncertainties. Validation of the level-2 SL_2_LST product, which became freely available on an operational basis from 5th July 2017 builds on an established validation protocol for satellite-based LST. This set of guidelines provides a standardized framework for structuring LST validation activities. The protocol introduces a four-pronged approach which can be summarised thus: i) in situ validation where ground-based observations are available; ii) radiance-based validation over sites that are homogeneous in emissivity; iii) intercomparison with retrievals from other satellite sensors; iv) time-series analysis to identify artefacts on an interannual time-scale. This multi-dimensional approach is a necessary requirement for assessing the performance of the LST algorithm for the Sea and LandSurface Temperature Radiometer (SLSTR) which is designed around biome

Remote sensing has provided us an opportunity to observe Earthlandsurface with a much higher resolution than any of GCM simulation. Due to scarcity of information for landsurface physical parameters, up-to-date GCMs still have large uncertainties in the coupled landsurface process modeling. One critical issue is a large amount of parameters used in their landsurface models. Thus remote sensing of landsurface spectral information can be used to provide information on these parameters or assimilated to decrease the model uncertainties. Satellite imager could observe the Earthlandsurface with optical, thermal and microwave bands. Some basic Earthlandsurface status (landsurface temperature, canopy height, canopy leaf area index, soil moisture etc.) has been produced with remote sensing technique, which already help scientists understanding Earthland and atmosphere interaction more precisely. However, there are some challenges when applying remote sensing variables to calculate global land-air heat and water exchange fluxes. Firstly, a global turbulent exchange parameterization scheme needs to be developed and verified, especially for global momentum and heat roughness length calculation with remote sensing information. Secondly, a compromise needs to be innovated to overcome the spatial-temporal gaps in remote sensing variables to make the remote sensing based landsurface fluxes applicable for GCM model verification or comparison. A flux network data library (more 200 flux towers) was collected to verify the designed method. Important progress in remote sensing of global land flux and evaporation will be presented and its benefits for GCM models will also be discussed. Some in-situ studies on the Tibetan Plateau and problems of landsurface process simulation will also be discussed.

As hydrologic scientists, we know that landsurface heterogeneity can have nuanced and sometimes dramatic impacts on the water cycle. Landsurface characteristics, including the structure and composition of vegetation and soil storage and drainage properties, alter how incoming precipitation is translated into streamflow and evapotranspiration. Landsurface heterogeneity can explain why this partitioning of incoming precipitation cannot always be computed by a simple water budget calculation. We also know that landsurface characteristics are dynamic - vegetation grows and changes with fire, disease and human actions and these changes will alter the partitioning of water - how much so, however depends itself on other site characteristics - soil water storage and the timing and magnitude of precipitation. This complex impact of space-time dynamics on the water cycle is something we need to effectively communicate to non-experts. For example, we may want to explain why sometimes forest management practices increase water availability but sometimes they don't - or why the impacts of urbanization or fire are location specific. If we do not communicate these dependencies we risk over-simplifying and eroding scientific credibility when observed effects don't match simple generalizations. On the other hand excessive detail can overwhelm and disengage audiences. So how do we help different communities public, private landowners, other scientists, NGOs, governments to better understand the role of space-time heterogeneity. To address this issue, we present some results from ongoing work that looks at the impact of fuel treatment of forest ecohydrology. This work stem from a collaboration between an ecohydrologic modeling team, social-scientists, a visual artist and compute graphics students. We use a coupled model, validated with field measurements, to show why spatial heterogeneity matters for understanding the impact of fuel treatments on the water cycle for the Sierra

The effective use of satellite observations of the landsurface is limited by the lack of high spatial resolution ground data sets for validation of satellite products. Recent large scale field experiments include FIFE, HAPEX-Sahel and BOREAS which provide us with data sets that have large spatial coverage and long time coverage. It is the objective of this paper to characterize the difference between the satellite estimates and the ground observations. This study and others along similar lines will help us in utilization of satellite retrieved data in large scale modeling studies.

Soil structure is largely absent in most standard sampling and measurements and in the subsequent parameterization of soil hydraulic properties deduced from soil maps and used in Earth System Models. The apparent omission propagates into the pedotransfer functions that deduce parameters of soil hydraulic properties primarily from soil textural information. Such simple parameterization is an essential ingredient in the practical application of any landsurface model. Despite the critical role of soil structure (biopores formed by decaying roots, aggregates, etc.) in defining soil hydraulic functions, only a few studies have attempted to incorporate soil structure into models. They mostly looked at the effects on preferential flow and solute transport pathways at the soil profile scale; yet, the role of soil structure in mediating large-scale fluxes remains understudied. Here, we focus on rectifying this gap and demonstrating potential impacts on surface and subsurface fluxes and system wide eco-hydrologic responses. The study proposes a systematic way for correcting the soil water retention and hydraulic conductivity functions—accounting for soil-structure—with major implications for near saturated hydraulic conductivity. Modification to the basic soil hydraulic parameterization is assumed as a function of biological activity summarized by Gross Primary Production. A land-surface model with dynamic vegetation is used to carry out numerical simulations with and without the role of soil-structure for 20 locations characterized by different climates and biomes across the globe. Including soil structure affects considerably the partition between infiltration and runoff and consequently leakage at the base of the soil profile (recharge). In several locations characterized by wet climates, a few hundreds of mm per year of surface runoff become deep-recharge accounting for soil-structure. Changes in energy fluxes, total evapotranspiration and vegetation productivity

The primary objective of this research is to develop a surface albedo model for the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). The primary test site is the Konza prairie, Kansas (U.S.A.), used by the International Satellite LandSurface Climatology Project (ISLSCP) in the First ISLSCP Field Experiment (FIFE). In this research, high spectral resolution field spectrometer data was analyzed to simulate AVHRR wavebands and to derive surface albedos. Development of a surface albedo algorithm was completed by analysing a combination of satellite, field spectrometer, and ancillary data. Estimated albedos from the field spectrometer data were compared to reference albedos derived using pyranometer data. Variations from surface anisotropy of reflected solar radiation were found to be the most significant albedo-related error. Additional error or sensitivity came from estimation of a shortwave mid-IR reflectance (1.3-4.0 micro-m) using the AVHRR red and near-IR bands. Errors caused by the use of AVHRR spectral reflectance to estimate both a total visible (0.4-0.7 micro-m) and near-IR (0.7-1.3 micro-m) reflectance were small. The solar spectral integration, using the derived ultraviolet, visible, near-IR and SW mid-IR reflectivities, was not sensitive to many clear-sky changes in atmospheric properties and illumination conditions.

This is a progress report of the 2nd phase of the project ENVISAT- LandSurface Processes, which has a 3-year scope. In this project, preparative research is carried out aiming at the retrieval of landsurface characteristics from the ENVISAT sensors MERIS and AATSR, for assimilation into a system for Numerical Weather Prediction (NWP). Where in the 1st phase a number of first shot experiments were carried out (aiming at gaining experience with the retrievals and data assimilation procedures), the current 2nd phase has put more emphasis on the assessment and improvement of the quality of the retrieved products. The forthcoming phase will be devoted mainly to the data assimilation experiments and the assessment of the added value of the future ENVISAT products for NWP forecast skill. Referring to the retrieval of albedo, leaf area index and atmospheric corrections, preliminary radiative transfer calculations have been carried out that should enable the retrieval of these parameters once AATSR and MERIS data become available. However, much of this work is still to be carried out. An essential part of work in this area is the design and implementation of software that enables an efficient use of MODTRAN(sub 4) radiative transfer code, and during the current project phase familiarization with these new components has been achieved. Significant progress has been made with the retrieval of component temperatures from directional ATSR-images, and the calculation of surface turbulent heat fluxes from these data. The impact of vegetation cover on the retrieved component temperatures appears manageable, and preliminary comparison of foliage temperature to air temperatures were encouraging. The calculation of surface fluxes using the SEBI concept,which includes a detailed model of the surface roughness ratio, appeared to give results that were in reasonable agreement with local measurements with scintillometer devices. The specification of the atmospheric boundary conditions

A landsurface model HAL is newly developed for MRI-ESM1. It is used for the CMIP simulations. HAL consists of three submodels: SiByl (vegetation), SNOWA (snow) and SOILA (soil) in the current version. It also contains a land coupler LCUP which connects some submodels and an atmospheric model. The vegetation submodel SiByl has surface vegetation processes similar to JMA/SiB (Sato et al. 1987, Hirai et al. 2007). SiByl has 2 vegetation layers (canopy and grass) and calculates heat, moisture, and momentum fluxes between the landsurface and the atmosphere. The snow submodel SNOWA can have any number of snow layers and the maximum value is set to 8 for the CMIP5 experiments. Temperature, SWE, density, grain size and the aerosol deposition contents of each layer are predicted. The snow properties including the grain size are predicted due to snow metamorphism processes (Niwano et al., 2011), and the snow albedo is diagnosed from the aerosol mixing ratio, the snow properties and the temperature (Aoki et al., 2011). The soil submodel SOILA can also have any number of soil layers, and is composed of 14 soil layers in the CMIP5 experiments. The temperature of each layer is predicted by solving heat conduction equations. The soil moisture is predicted by solving the Darcy equation, in which hydraulic conductivity depends on the soil moisture. The land coupler LCUP is designed to enable the complicated constructions of the submidels. HAL can include some competing submodels (precise and detailed ones, and simpler ones), and they can run at the same simulations. LCUP enables a 2-step model validation, in which we compare the results of the detailed submodels with the in-situ observation directly at the 1st step, and follows the comparison between them and those of the simpler ones at the 2nd step. When the performances of the detailed ones are good, we can improve the simpler ones by using the detailed ones as reference models.

This study analyzed land use and land cover changes and their impact on landsurface temperature using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta. Six Landsat images comprising two time series were used to calculate the landsurface temperature and correlated vegetation indices. The Yellow River Delta area has expanded substantially because of the deposited sediment carried from upstream reaches of the river. Between 1986 and 2015, approximately 35% of the land use area of the Yellow River Delta has been transformed into salterns and aquaculture ponds. Overall, land use conversion has occurred primarily from poorly utilized land into highly utilized land. To analyze the variation of landsurface temperature, a mono-window algorithm was applied to retrieve the regional landsurface temperature. The results showed bilinear correlation between landsurface temperature and the vegetation indices (i.e., Normalized Difference Vegetation Index, Adjusted-Normalized Vegetation Index, Soil-Adjusted Vegetation Index, and Modified Soil-Adjusted Vegetation Index). Generally, values of the vegetation indices greater than the inflection point mean the landsurface temperature and the vegetation indices are correlated negatively, and vice versa. Landsurface temperature in coastal areas is affected considerably by local seawater temperature and weather conditions.

Uncertainties in the retrievals of microwaveland-surface emissivities are quantified over two types of landsurfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including the Special Sensor Microwave Imager, the Tropical Rainfall Measuring Mission Microwave Imager, and the Advanced Microwave Scanning Radiometer for Earth Observing System, are studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land-surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors inthe retrievals. Generally, these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1%-4% (3-12 K) over desert and 1%-7% (3-20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5%-2% (2-6 K). In particular, at 85.5/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are most likely caused by rain/cloud contamination, which can lead to random errors up to 10-17 K under the most severe conditions.

The Advanced Land Imager (ALI) is the primary instrument on the Earth Observing-1 spacecraft (EO-1) and was developed under NASA's New Millennium Program (NMP). The NMP mission objective is to flight-validate advanced technologies that will enable dramatic improvements in performance, cost, mass, and schedule for future, Landsat-like, Earth Science Enterprise instruments. ALI contains a number of innovative features designed to achieve this objective. These include the basic instrument architecture, which employs a push-broom data collection mode, a wide field-of-view optical design, compact multi-spectral detector arrays, non-cryogenic HgCdTe for the short wave infrared bands, silicon carbide optics, and a multi-level solar calibration technique. The sensor includes detector arrays that operate in ten bands, one panchromatic, six VNIR and three SWIR, spanning the range from 0.433 to 2.35 μm. Launched on November 21, 2000, ALI instrument performance was monitored during its first year on orbit using data collected during solar, lunar, stellar, and earth observations. This paper will provide an overview of EO-1 mission activities during this period. Additionally, the on-orbit spatial and radiometric performance of the instrument will be compared to pre-flight measurements and the temporal stability of ALI will be presented.

The Earth's landsurface, including its biomass, is an integral part of the Earth's weather and climate system. Landsurface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a landsurface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic landsurface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in landsurface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface

Understanding the spectral and photometric variability of the Earth and the rest of the solar system planets has become of utmost importance for the future characterization of rocky exoplanets. As this is not only interesting at present times but also along the planetary evolution, we studied the effect that the evolution of microbial mats and plants over land has had on the way our planet looks from afar. As life evolved, continental surfaces changed gradually and non-uniformly from deserts through microbial mats to land plants, modifying the reflective properties of the ground and most likely the distribution of moisture and cloudiness. Here, we used a radiative transfer model of the Earth, together with geological paleo-records of the continental distribution and a reconstructed cloud distribution, to simulate the visible and near-IR radiation reflected by our planet as a function of Earth's rotation. We found that the evolution from deserts to microbial mats and to land plants produces detectable changes in the globally averaged Earth's reflectance. The variability of each surface type is located in different bands and can induce reflectance changes of up to 40% in period of hours. We conclude that by using photometric observations of an Earth-like planet at different photometric bands it would be possible to discriminate between different surface types. While recent literature proposes the red-edge feature of vegetation near 0.7 μm as a signature for land plants, observations in near-IR bands can be equally or even better suited for this purpose.

We investigated relations between slip-surface geometry and deformational structures and hydrologic features at the Montaguto earth flow in southern Italy between 1954 and 2010. We used 25 boreholes, 15 static cone-penetration tests, and 22 shallow-seismic profiles to define the geometry of basal- and lateral-slip surfaces; and 9 multitemporal maps to quantify the spatial and temporal distribution of normal faults, thrust faults, back-tilted surfaces, strike-slip faults, flank ridges, folds, ponds, and springs. We infer that the slip surface is a repeating series of steeply sloping surfaces (risers) and gently sloping surfaces (treads). Stretching of earth-flow material created normal faults at risers, and shortening of earth-flow material created thrust faults, back-tilted surfaces, and ponds at treads. Individual pairs of risers and treads formed quasi-discrete kinematic zones within the earth flow that operated in unison to transmit pulses of sediment along the length of the flow. The locations of strike-slip faults, flank ridges, and folds were not controlled by basal-slip surface topography but were instead dependent on earth-flow volume and lateral changes in the direction of the earth-flow travel path. The earth-flow travel path was strongly influenced by inactive earth-flow deposits and pre-earth-flow drainages whose positions were determined by tectonic structures. The implications of our results that may be applicable to other earth flows are that structures with strikes normal to the direction of earth-flow motion (e.g., normal faults and thrust faults) can be used as a guide to the geometry of basal-slip surfaces, but that depths to the slip surface (i.e., the thickness of an earth flow) will vary as sediment pulses are transmitted through a flow.

Hydrological models have been calibrated and validated using catchment streamflows. However, using a point measurement does not guarantee correct spatial distribution of model computed heat fluxes, soil moisture and surface temperatures. With the advent of satellites in the late 70s, surface temperature is being measured two to four times a day from various satellite sensors and different platforms. The purpose of this paper is to demonstrate use of satellite surface temperature in (a) validation of model computed surface temperatures and (b) assimilation of satellite surface temperatures into a hydrological model in order to improve the prediction accuracy of soil moistures and heat fluxes. The assimilation is carried out by comparing the satellite and the model produced surface temperatures and setting the "true"temperature midway between the two values. Based on this "true" surface temperature, the physical relationships of water and energy balance are used to reset the other variables. This is a case of nudging the water and energy balance variables so that they are consistent with each other and the true" surface temperature. The potential of this assimilation scheme is demonstrated in the form of various experiments that highlight the various aspects. This study is carried over the Red-Arkansas basin in the southern United States (a 5 deg X 10 deg area) over a time period of a year (August 1987 - July 1988). The landsurface hydrological model is run on an hourly time step. The results show that satellite surface temperature assimilation improves the accuracy of the computed surface soil moisture remarkably.

Spectral landsurface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. Recent observations of diffuse bihemispherical (white-sky) and direct beam directional hemispherical (black-sky ) landsurface albedo included in the MOD43B3 product from MODIS instruments aboard NASA's Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal characteristics. Cloud and seasonal snow cover, however, curtail retrievals to approximately half the global landsurfaces on an annual equal-angle basis, precluding MOD43B3 albedo products from direct inclusion in some research projects and production environments.

One of the main objectives of the Sentinel-3 mission is to measure sea- and land-surface temperature with high-end accuracy and reliability in support of environmental and climate monitoring in an operational context. Calibration and validation are thus key criteria for operationalization within the framework of the Sentinel-3 Mission Performance Centre (S3MPC).Landsurface temperature (LST) has a long heritage of satellite observations which have facilitated our understanding of landsurface and climate change processes, such as desertification, urbanization, deforestation and land/atmosphere coupling. These observations have been acquired from a variety of satellite instruments on platforms in both low-earth orbit and in geostationary orbit. Retrieval accuracy can be a challenge though; surface emissivities can be highly variable owing to the heterogeneity of the land, and atmospheric effects caused by the presence of aerosols and by water vapour absorption can give a bias to the underlying LST. As such, a rigorous validation is critical in order to assess the quality of the data and the associated uncertainties. The Sentinel-3 Cal-Val Plan for evaluating the level-2 SL_2_LST product builds on an established validation protocol for satellite-based LST. This set of guidelines provides a standardized framework for structuring LST validation activities, and is rapidly gaining international recognition. The protocol introduces a four-pronged approach which can be summarised thus: i) in situ validation where ground-based observations are available; ii) radiance-based validation over sites that are homogeneous in emissivity; iii) intercomparison with retrievals from other satellite sensors; iv) time-series analysis to identify artefacts on an interannual time-scale. This multi-dimensional approach is a necessary requirement for assessing the performance of the LST algorithm for SLSTR which is designed around biome-based coefficients, thus emphasizing the importance of

We have developed a set of metrics for measuring the feedback loop between the landsurface moisture state and the atmosphere globally on an interannual time scale. These metrics consider both the forcing of terrestrial water storage (TWS) on subsequent atmospheric conditions as well as the response of TWS to antecedent atmospheric conditions. We designed our metrics to take advantage of more than one decade's worth of satellite observations of TWS from the Gravity Recovery and Climate Experiment (GRACE) along with atmospheric variables from the Atmospheric Infrared Sounder (AIRS), the Global Precipitation Climatology Project (GPCP), and Clouds and the Earths Radiant Energy System (CERES). Metrics derived from spaceborne observations were used to evaluate the strength of the feedback loop in the Community Earth System Model (CESM) Large Ensemble (LENS) and in several models that contributed simulations to Phase 5 of the Coupled Model Intercomparison Project (CMIP5). We found that both forcing and response limbs of the feedback loop were generally stronger in tropical and temperate regions in CMIP5 models and even more so in LENS compared to satellite observations. Our analysis suggests that models may overestimate the strength of the feedbacks between the landsurface and the atmosphere, which is consistent with previous studies conducted across different spatial and temporal scales.

Landsurface hydrology for the Off-line Land-surface GEOS Analysis (OLGA) system and Goddard Earth Observing System (GEOS-1) Data Assimilation System (DAS) has been examined using a river routing model. The GEOS-1 DAS land-surface parameterization is very simple, using an energy balance prediction of surface temperature and prescribed soil water. OLGA uses near-surface atmospheric data from the GEOS-1 DAS to drive a more comprehensive parameterization of the land-surface physics. The two global systems are evaluated using a global river routing model. The river routing model uses climatologic surface runoff from each system to simulate the river discharge from global river basins, which can be compared to climatologic river discharge. Due to the soil hydrology, the OLGA system shows a general improvement in the simulation of river discharge compared to the GEOS-1 DAS. Snowmelt processes included in OLGA also have a positive effect on the annual cycle of river discharge and source runoff. Preliminary tests of a coupled land-atmosphere model indicate improvements to the hydrologic cycle compared to the uncoupled system. The river routing model has provided a useful tool in the evaluation of the GCM hydrologic cycle, and has helped quantify the influence of the more advanced landsurface model.

Surface air temperature (T(sub a)) is a critical variable in the energy and water cycle of the Earth.atmosphere system and is a key input element for hydrology and landsurface models. This is a preliminary study to evaluate estimation of T(sub a) from satellite remotely sensed landsurface temperature (T(sub s)) by using MODIS-Terra data over two Eurasia regions: northern China and fUSSR. High correlations are observed in both regions between station-measured T(sub a) and MODIS T(sub s). The relationships between the maximum T(sub a) and daytime T(sub s) depend significantly on land cover types, but the minimum T(sub a) and nighttime T(sub s) have little dependence on the land cover types. The largest difference between maximum T(sub a) and daytime T(sub s) appears over the barren and sparsely vegetated area during the summer time. Using a linear regression method, the daily maximum T(sub a) were estimated from 1 km resolution MODIS T(sub s) under clear-sky conditions with coefficients calculated based on land cover types, while the minimum T(sub a) were estimated without considering land cover types. The uncertainty, mean absolute error (MAE), of the estimated maximum T(sub a) varies from 2.4 C over closed shrublands to 3.2 C over grasslands, and the MAE of the estimated minimum Ta is about 3.0 C.

Uncertainties in land parameters could have important impacts on simulated water and energy fluxes and landsurface states, which will consequently affect atmospheric and biogeochemical processes. Therefore, quantification of such parameter uncertainties using a landsurface model is the first step towards better understanding of predictive uncertainty in Earth system models. In this study, we applied a random-sampling, high-dimensional model representation (RS-HDMR) method to analyze the sensitivity of simulated photosynthesis, surface energy fluxes and surface hydrological components to selected land parameters in version 4.5 of the Community Land Model (CLM4.5). Because of the large computational expense of conducting ensembles of global gridded model simulations, we used the results of a previous cluster analysis to select one thousand representative land grid cells for simulation. Plant functional type (PFT)-specific uniform prior ranges for land parameters were determined using expert opinion and literature survey, and samples were generated with a quasi-Monte Carlo approach-Sobol sequence. Preliminary analysis of 1024 simulations suggested that four PFT-dependent parameters (including slope of the conductance-photosynthesis relationship, specific leaf area at canopy top, leaf C:N ratio and fraction of leaf N in RuBisco) are the dominant sensitive parameters for photosynthesis, surface energy and water fluxes across most PFTs, but with varying importance rankings. On the other hand, for surface ans sub-surface runoff, PFT-independent parameters, such as the depth-dependent decay factors for runoff, play more important roles than the previous four PFT-dependent parameters. Further analysis by conditioning the results on different seasons and years are being conducted to provide guidance on how climate variability and change might affect such sensitivity. This is the first step toward coupled simulations including biogeochemical processes, atmospheric processes

Atmospheric chaos severely limits the predictability of precipitation on subseasonal to interannual timescales. Hope for accurate long-term precipitation forecasts lies with simulating atmospheric response to components of the Earth system, such as the ocean, that can be predicted beyond a couple of weeks. Indeed, seasonal forecasts centers now rely heavily on forecasts of ocean circulation. Soil moisture, another slow component of the Earth system, is relatively ignored by the operational seasonal forecasting community. It is starting, however, to garner more attention. Soil moisture anomalies can persist for months. Because these anomalies can have a strong impact on evaporation and other surface energy fluxes, and because the atmosphere may respond consistently to anomalies in the surface fluxes, an accurate soil moisture initialization in a forecast system has the potential to provide additional forecast skill. This potential has motivated a number of atmospheric general circulation model (AGCM) studies of soil moisture and its contribution to variability in the climate system. Some of these studies even suggest that in continental midlatitudes during summer, oceanic impacts on precipitation are quite small relative to soil moisture impacts. The model results, though, are strongly model-dependent, with some models showing large impacts and others showing almost none at all. A validation of the model results with observations thus naturally suggests itself, but this is exceedingly difficult. The necessary contemporaneous soil moisture, evaporation, and precipitation measurements at the large scale are virtually non-existent, and even if they did exist, showing statistically that soil moisture affects rainfall would be difficult because the other direction of causality - wherein rainfall affects soil moisture - is unquestionably active and is almost certainly dominant. Nevertheless, joint analyses of observations and AGCM results do reveal some suggestions of

Aerosols direct and indirect effects on the Earth's climate are widely recognized but have yet to be adequately quantified. Difficulties arise due to the very high spatial and temporal variability of aerosols, which is a major cause of uncertainties in radiative forcing studies. The effective monitoring of the global aerosol distribution is only made possible by satellite monitoring and this is the reason why the interest in aerosol observations from satellite passive radiometers is steadily increasing. From the point of view of the study of landsurfaces, the atmosphere with its constituents represents an obscurant whose effects should be as much as possible eliminated, being this process sometimes referred to as atmospheric correction. In absence of clouds and using spectral intervals where gas absorption can be avoided to a great extent, only the aerosol effect remains to be corrected. The monitoring of the aerosol particles present in the atmosphere is then crucial to succeed in doing an accurate atmospheric correction, otherwise the surface properties may be inadequately characterised. However, the atmospheric correction over landsurfaces turns out to be a difficult task since surface reflection competes with the atmospheric component of the signal. On the other hand, a single mean pre-established aerosol characterisation would not be sufficient for this purpose due to very high spatial and temporal variability of aerosols and their unpredictability, especially what concerns particulary intense "events" such as biomass burning and forest fires, desert dust episodes and volcanic eruptions. In this context, an operational methodology has been developed at the University of Evora - Evora Geophysics Centre (CGE), in the framework of the Satellite Application Facility for LandSurface Analysis - Land SAF, to derive an Aerosol Product from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, flying on the Geostationary (GEO) satellite system Meteosat-8

The Western EarthSurface Processes Team (WESPT) of the U.S. Geological Survey (USGS) conducts geologic mapping and related topical earth-science studies in the Western United States. This work is focused on areas where modern geologic maps and associated earth-science data are needed to address key societal and environmental issues, such as ground-water quality, landslides and other potential geologic hazards, and land-use decisions. Areas of primary emphasis in 2001 included southern California, the San Francisco Bay region, the Pacific Northwest, and the Las Vegas urban corridor. The team has its headquarters in Menlo Park, California, and maintains smaller field offices at several other locations in the Western United States. The results of research conducted by the WESPT are released to the public as a variety of databases, maps, text reports, and abstracts, both through the internal publication system of the USGS and in diverse external publications such as scientific journals and books. This report lists publications of the WESPT released in 2001, as well as additional 1999 and 2000 publications that were not included in the previous list (USGS Open-File Report 00–215 and USGS Open-File Report 01–198). Most of the publications listed were authored or coauthored by WESPT staff. The list also includes some publications authored by non-USGS cooperators with the WESPT, as well as some authored by USGS staff outside the WESPT in cooperation with WESPT projects. Several of the publications listed are available on the World Wide Web; for these, URL addresses are provided. Many of these web publications are USGS Open-File Reports that contain large digital databases of geologic map and related information.

Introduction The Western EarthSurface Processes Team (WESPT) of the U.S. Geological Survey (USGS) conducts geologic mapping, earth-surface process investigations, and related topical earth science studies in the western United States. This work is focused on areas where modern geologic maps and associated earth-science data are needed to address key societal and environmental issues such as ground-water quality, landslides and other potential geologic hazards, and land-use decisions. Areas of primary emphasis in 2005 included southern California, the San Francisco Bay region, the Mojave Desert, the Colorado Plateau region of northern Arizona, and the Pacific Northwest. The team has its headquarters in Menlo Park, California, and maintains smaller field offices at several other locations in the western United States. The results of research conducted by the WESPT are released to the public as a variety of databases, maps, text reports, and abstracts, both through the internal publication system of the USGS and in diverse external publications such as scientific journals and books. This report lists publications of the WESPT released in 2005 as well as additional 2002, 2003, and 2004 publications that were not included in the previous lists (USGS Open-File Reports 03-363, 2004- 1267, 2005-1362). Most of the publications listed were authored or coauthored by WESPT staff. The list also includes some publications authored by non-USGS cooperators with the WESPT, as well as some authored by USGS staff outside the WESPT in cooperation with WESPT projects. Several of the publications listed are available on the World Wide Web; for these, URL addresses are provided. Many of these web publications are USGS Open-File reports that contain large digital databases of geologic map and related information. Information on ordering USGS publications can be found on the World Wide Web at http://www.usgs.gov/pubprod/, or by calling 1-888-ASK-USGS. The U.S. Geological Survey's web

The 380 nm radiance measurements of TOMS (Total Ozone Mapping Spectrometer) have been converted into a global data set of daily (1979 to 1992) Lambert equivalent reflectivities R of the Earth's surface and boundary layer (clouds, aerosols, surface haze, and snow/ice). Since UV surface reflectivity is between 2 and 8% for both land and water during all seasons of the year (except for ice and snow cover), reflectivities larger than the surface value indicates the presence of clouds, haze, or aerosols in the satellite field of view. Statistical analysis of 14 years of daily data show that most snow/ice-free regions of the Earth have their largest fraction of days each year when the reflectivity is low (R less than 10%). The 380 nm reflectivity data shows that the true surface reflectivity is 2 to 3% lower than the most frequently occurring reflectivity value for each TOMS scene. The most likely cause of this could be a combination of frequently occurring boundary-layer water or aerosol haze. For most regions, the observation of extremely clear conditions needed to estimate the surface reflectivity from space is a comparatively rare occurrence. Certain areas (e.g., Australia, southern Africa, portions of northern Africa) are cloud-free more than 80% of the year, which exposes these regions to larger amounts of UV radiation than at comparable latitudes in the Northern Hemisphere. Regions over rain-forests, jungle areas, Europe and Russia, the bands surrounding the Arctic and Antarctic regions, and many ocean areas have significant cloud cover (R greater than 15%) more than half of each year. In the low to middle latitudes, the areas with the heaviest cloud cover (highest reflectivity for most of the year) are the forest areas of northern South America, southern Central America, the jungle areas of equatorial Africa, and high mountain regions such as the Himalayas or the Andes. The TOMS reflectivity data show the presence of large nearly clear ocean areas and the effects

LandSurface Models (LSMs) are key elements in guiding adaptation to the changing water cycle and the starting points to develop a global hyper-resolution model of the terrestrial water, energy and biogeochemical cycles. However, before this potential is realised, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. An important limitation is the simplistic or non-existent representation of the deep subsurface in LSMs; and another is the lack of connection of LSM parameterisations to relevant hydrological information. In this context, the paper uses a case study of the JULES (Joint UK Land Environmental Simulator) LSM applied to the Kennet region in Southern England. The paper explores the assumptions behind JULES hydrology, adapts the model structure and optimises the coupling with the ZOOMQ3D regional groundwater model. The analysis illustrates how three types of information can be used to improve the model's hydrology: a) observations, b) regionalized information, and c) information from an independent physics-based model. It is found that: 1) coupling to the groundwater model allows realistic simulation of streamflows; 2) a simple dynamic lower boundary improves upon JULES' stationary unit gradient condition; 3) a 1D vertical flow in the unsaturated zone is sufficient; however there is benefit in introducing a simple dual soil moisture retention curve; 4) regionalized information can be used to describe soil spatial heterogeneity. It is concluded that relatively simple refinements to the hydrology of JULES and its parameterisation method can provide a substantial step forward in realising its potential as a high-resolution multi-purpose model.

As of 2013, the Noah LandSurface Model (LSM) version 2.7.1 was implemented in a new global model being developed at the Korea Institute of Atmospheric Prediction Systems (KIAPS). This landsurface scheme is further refined in two aspects, by adding new physical processes and by updating surface input parameters. Thus, the treatment of glacier land, sea ice, and snow cover are addressed more realistically. Inconsistencies in the amount of absorbed solar flux at ground level by the landsurface and radiative processes are rectified. In addition, new parameters are available by using 1-km land cover data, which had usually not been possible at a global scale. Landsurface albedo/emissivity climatology is newly created using Moderate-Resolution Imaging Spectroradiometer (MODIS) satellitebased data and adjusted parameterization. These updates have been applied to the KIAPS-developed model and generally provide a positive impact on near-surface weather forecasting.

The land-atmosphere interface is where humans primarily operate. Humans modify the landsurface in many ways that influence the fluxes of energy and trace gases between land and atmosphere. Their emissions change the chemical composition of the atmosphere and anthropogenic aerosols change the radiative balance of the globe directly by scattering sunlight back to space and indirectly by changing the properties of clouds. Feedback loops among all these processes, land, the atmosphere, and biogeochemical cycles of nutrients and trace gases extend the human influence even further. Over the last decade, the importance of land-atmosphere processes and feedbacks in the Earth System has been shown on many levels and with multiple approaches, and a number of publications have shown the crucial role of the terrestrial ecosystems as regulators of climate [1-6]. Modellers have clearly shown the effect of missing land cover changes and other feedback processes and regional characteristics in current climate models and recommended actions to improve them [7-11]. Unprecedented insights of the long-term net impacts of aerosols on clouds and precipitation have also been provided [12-14]. Land-cover change has been emphasized with model intercomparison projects that showed that realistic land-use representation was essential in landsurface modelling [11, 15]. Crucially important tools in this research have been the networks of long-term flux stations and large-scale land-atmosphere observation platforms that are also beginning to combine remote sensing techniques with ground observations [16-20]. Human influence has always been an important part of land-atmosphere science but in order to respond to the new challenges of global sustainability, closer ties with social science and economics groups will be necessary to produce realistic estimates of land use and anthropogenic emissions by analysing future population increase, migration patterns, food production allocation, land

Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales. Discernible human impacts on the Earth's climate system have been revealed by using statistical frameworks of detection-attribution. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts.

Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales. Discernible human impacts on the Earth's climate system have been revealed by using statistical frameworks of detection-attribution. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts.

This special issue has highlighted recent and innovative methods and results that integrate observations and AQ3 modelling analyses of regional to global aspect of biophysical and biogeochemical interactions of land-cover change with the climate system. Both the Earth System and the Integrated Assessment modeling communities recognize the importance of an accurate representation of land use and land-cover change to understand and quantify the interactions and feedbacks with the climate and socio-economic systems, respectively. To date, cooperation between these communities has been limited. Based on common interests, this work discusses research priorities in representing land use and land-cover change for improvedmore » collaboration across modelling, observing and measurement communities. Major research topics in land use and land-cover change are those that help us better understand (1) the interaction of land use and land cover with the climate system (e.g. carbon cycle feedbacks), (2) the provision of goods and ecosystem services by terrestrial (natural and anthropogenic) land-cover types (e.g. food production), (3) land use and management decisions and (4) opportunities and limitations for managing climate change (for both mitigation and adaptation strategies).« less

Articles are presented on the utilization of remote sensing data from NASA programs involving LANDSAT, the Skylab Earth resources experiment package, and aircraft, as well as from other data acquisition programs. Emphasis is placed on land use and marine resources.

Past studies have demonstrated how changes in vegetation can impact the atmosphere; however, it is often difficult to identify the exact physical pathway through which vegetation changes drive an atmospheric response. Surface properties (such as vegetation color, or height) control surface energy fluxes, which feed back on the atmosphere on both local and global scales by modifying temperatures, cloud cover, and energy gradients. Understanding how landsurface properties influence energy fluxes is crucial for improving our understanding of how vegetation change - past, present, and future - impacts the atmosphere, global climate, and people. We explore the sensitivity of the atmosphere to perturbations of three landsurface properties - albedo, roughness, and evaporative resistance - using an idealized land model coupled to an Earth System Model. We derive a relationship telling us how large a change in each surface property is required to drive a local 0.1 K change in 2m air temperature. Using this idealized framework, we are able to separate the influence on the atmosphere of each individual surface property. We demonstrate that the impact of each surface property on the atmosphere is spatially variable - that is, a similar change in vegetation can have different climate impacts if made in different locations. This analysis not only improves our understanding of how the land system can influence climate, but also provides us with a set of theoretical limits on the potential climate impact of arbitrary vegetation change (natural or anthropogenic).

This paper reports on boreholes from 4.5 to greater than 10 kilometers deep that are pushing back the boundaries of earth science as they yield information that is used to refine seismic surveys, chart the evolution of sedimentary basins and shield volcanos, and uncover important clues on the origin and migration of mantle-derived water and gas.

In landsurface modeling, it is almost impossible to simulate the landsurface processes without any error because the earth system is highly complex and the physics of the land processes has not yet been understood sufficiently. In most cases, people want to know not only the model output but also the uncertainty in the modeling, to estimate how reliable the modeling is. Ensemble perturbation is an effective way to estimate the uncertainty in landsurface modeling, since landsurface models are highly nonlinear which makes the analytical approach not applicable in this estimation. The ideal perturbation noise is zero mean Gaussian distribution, however, this requirement can't be satisfied if the perturbed variables in landsurface model have physical boundaries because part of the perturbation noises has to be removed to feed the landsurface models properly. Two different perturbation methods are employed in our study to investigate their impact on quantifying landsurface modeling uncertainty base on the Land Information System (LIS) framework developed by NASA/GSFC land team. One perturbation method is the built-in algorithm named "STATIC" in LIS version 5; the other is a new perturbation algorithm which was recently developed to minimize the overall bias in the perturbation by incorporating additional information from the whole time series for the perturbed variable. The statistical properties of the perturbation noise generated by the two different algorithms are investigated thoroughly by using a large ensemble size on a NASA supercomputer and then the corresponding uncertainty estimates based on the two perturbation methods are compared. Their further impacts on data assimilation are also discussed. Finally, an optimal perturbation method is suggested.

For given climatic rates of precipitation and potential evaporation, the landsurface hydrology parameterizations of atmospheric general circulation models will maintain soil-water storage conditions that balance the moisture input and output. The surface relative soil saturation for such climatic conditions serves as a measure of the landsurface parameterization state under a given forcing. The equilibrium value of this variable for alternate parameterizations of landsurface hydrology are determined as a function of climate and the sensitivity of the surface to shifts and changes in climatic forcing are estimated.

Since the implementation of the federal Surface Mining Control and Reclamation Act of 1977 (SMCRA) in May of 1978, many opportunities have been lost for the reforestation of surface mines in the eastern United States. Research has shown that excessive compaction of spoil material in the backfilling and grading process is the biggest impediment to the establishment of productive forests as a post-mining land use (Ashby, 1998, Burger et al., 1994, Graves et al., 2000). Stability of mine sites was a prominent concern among regulators and mine operators in the years immediately following the implementation of SMCRA. These concerns resultedmore » in the highly compacted, flatly graded, and consequently unproductive spoils of the early post-SMCRA era. However, there is nothing in the regulations that requires mine sites to be overly compacted as long as stability is achieved. It has been cultural barriers and not regulatory barriers that have contributed to the failure of reforestation efforts under the federal law over the past 27 years. Efforts to change the perception that the federal law and regulations impede effective reforestation techniques and interfere with bond release must be implemented. Demonstration of techniques that lead to the successful reforestation of surface mines is one such method that can be used to change perceptions and protect the forest ecosystems that were indigenous to these areas prior to mining. The University of Kentucky initiated a large-scale reforestation effort to address regulatory and cultural impediments to forest reclamation in 2003. During the three years of this project 383,000 trees were planted on over 556 acres in different physiographic areas of Kentucky (Table 1, Figure 1). Species used for the project were similar to those that existed on the sites before mining was initiated (Table 2). A monitoring program was undertaken to evaluate growth and survival of the planted species as a function of spoil characteristics and

There has recently been a general realization that more sophisticated modeling of land-surface processes can be important for mesoscale meteorology models. Land-surface models (LSMs) have long been important components in global-scale climate models because of their more compl...

There is a significant lack of continuously measured ET data for multiple landsurfacesÂ in the same area to be able to make comparisons of water use rates of different agroecosystems.Â This research presentation will provide continuous evapotranspiration andÂ other surface energy balance variables measured above multiple land use and managementÂ practices.

Landsurface microwave emissivity affects remote sensing of both the atmosphere and the landsurface. The dynamical behavior of microwave emissivity over a very diverse sample of landsurface types is studied. With seven years of satellite measurements from AMSR-E, we identified various dynamical regimes of the landsurface emission. In addition, we used two radiative transfer models (RTMs), the Community Radiative Transfer Model (CRTM) and the Community Microwave Emission Modeling Platform (CMEM), to simulate landsurface emissivity dynamics. With both CRTM and CMEM coupled to NASA's Land Information System, global-scale landsurface microwave emissivities were simulated for five years, and evaluated against AMSR-E observations. It is found that both models have successes and failures over various types of landsurfaces. Among them, the desert shows the most consistent underestimates (by approx. 70-80%), due to limitations of the physical models used, and requires a revision in both systems. Other snow-free surface types exhibit various degrees of success and it is expected that parameter tuning can improve their performances.

More than 600,000 ha (1.5 million ac) of mostly forested land in the Appalachian region were surface mined for coal under the Surface Mining Control and Reclamation Act. Today, these lands are largely unmanaged and covered with persistent herbaceous species, such as fescue (Festuca spp.) and sericea lespedeza (Lespedeza cuneata [Dum. Cours.] G. Don,) and a mix of...

This paper describes NASA's initial steps for identifying and evaluating candidate Exploration Zones (EZs) and Regions of Interests (ROIs) for the first human crews that will explore the surface of Mars. NASA's current effort to define the exploration of this planet by human crews, known as the Evolvable Mars Campaign (EMC), provides the context in which these EZs and ROIs are being considered. The EMC spans all aspects of a human Mars mission including launch from Earth, transit to and from Mars, and operations on the surface of Mars. Studies related to Mars surface operations and related system capabilities have led to the current definition of an EZ as well as ROIs. An EZ is a collection of ROIs that are located within approximately 100 kilometers of a centralized landing site. ROIs are areas that are relevant for scientific investigation and/or development/maturation of capabilities and resources necessary for a sustainable human presence. The EZ also contains one or more landing sites and a habitation site that will be used by multiple human crews during missions to explore and utilize the ROIs within the EZ. With the EMC as a conceptual basis, the EZ model has been refined to a point where specific site selection criteria for scientific exploration and in situ resource utilization can be defined. In 2015 these criteria were distributed to the planetary sciences community and the in situ resource utilization and civil engineering communities as part of a call for EZ proposals. The resulting "First Landing Site/Exploration Zone Workshop for Human Missions to the Surface of Mars" was held in October 2015 during which 47 proposals for EZs and ROIs were presented and discussed. Proposed locations spanned all longitudes and all allowable latitudes (+/- 50 degrees). Proposed justification for selecting one of these EZs also spanned a significant portion of the scientific and resource criteria provided to the community. Workshop results will be used to prepare for

Landsurface temperature (LST) determines the radiance emitted by the surface and hence is an important boundary condition of the energy balance. In urban areas, detailed knowledge about the diurnal cycle in LST can contribute to understand the urban heat island (UHI). Although the increased surface temperatures compared to the surrounding rural areas (surface urban heat island, SUHI) have been measured by satellites and analysed for several decades, an operational SUHI monitoring is still not available due to the lack of sensors with appropriate spatiotemporal resolution. While sensors on polar orbiting satellites are still restricted to approx. 100 m spatial resolution and coarse temporal coverage (about 1-2 weeks), sensors on geostationary platforms have high temporal (several times per hour) and poor spatial resolution (>3 km). Further, all polar orbiting satellites have a similar equator crossing time and hence the SUHI can at best be observed at two times a day. A downscaling DS scheme for LST from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard the geostationary meteorological Meteosat 8 to spatial resolutions between 100 and 1000 m was developed and tested for Hamburg. Various data were tested as predictors, including multispectral data and derived indices, morphological parameters from interferometric SAR and multitemporal thermal data. All predictors were upscaled to the coarse resolution approximating the point spread function of SEVIRI. Then empirical relationships between the predictors and LST were derived which are then transferred to the high resolution domain, assuming they are scale invariant. For validation LST data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Enhanced Thematic Mapper Plus (ETM+) for two dates were used. Aggregated parameters from multi-temporal thermal data (in particular annual cycle parameters and principal components) proved particularly suitable. The results

The U.S. wind industry has experienced a remarkably rapid expansion of capacity in recent years and this rapid growth is expected to continue in the future. While converting wind's kinetic energy into electricity, wind turbines modify surface-atmosphere exchanges and transfer of energy, momentum, mass and moisture within the atmosphere. These changes, if spatially large enough, may have noticeable impacts on local to regional weather and climate. Here we present observational evidence for such impacts based on analyses of satellite derived landsurface temperature (LST) data at ~1.1 km for the period of 2003-2011 over a region in West-Central Texas, where four of the world's largest wind farms are located. Our results show a warming effect of up to 0.7 degrees C at nighttime for the 9-year period during which data was collected, over wind farms relative to nearby non wind farm regions and this warming is gradually enhanced with time, while the effect at daytime is small. The spatial pattern and magnitude of this warming effect couple very well with the geographic distribution of wind turbines and such coupling is stronger at nighttime than daytime and in summer than winter. These results suggest that the warming effect is very likely attributable to the development of wind farms. This inference is consistent with the increasing number of operational wind turbines with time during the study period, the diurnal and seasonal variations in the frequency of wind speed and direction distribution, and the changes in near-surface atmospheric boundary layer conditions due to wind farm operations. Figure 1: Nighttime landsurface temperature (LST, C) differences between 2010 and 2003 (2010 minus 2003) in summer (June-July-August). Pixels with plus symbol have at least one wind turbine. A regional mean value (0.592 C) was removed to emphasize the relative LST changes at pixel level and so the resulting warming or cooling rate represents a change relative to the regional mean

Describes an activity which incorporates topographic map interpretation, soils analysis, hydrogeology, and local geology in a five-week series of exercises for an introductory college earth science class. (CP)

Environmental medicine generally addresses environmental factors with a negative impact on human health. However, emerging scientific research has revealed a surprisingly positive and overlooked environmental factor on health: direct physical contact with the vast supply of electrons on the surface of the Earth. Modern lifestyle separates humans from such contact. The research suggests that this disconnect may be a major contributor to physiological dysfunction and unwellness. Reconnection with the Earth's electrons has been found to promote intriguing physiological changes and subjective reports of well-being. Earthing (or grounding) refers to the discovery of benefits—including better sleep and reduced pain—from walking barefoot outside or sitting, working, or sleeping indoors connected to conductive systems that transfer the Earth's electrons from the ground into the body. This paper reviews the earthing research and the potential of earthing as a simple and easily accessed global modality of significant clinical importance. PMID:22291721

Environmental medicine generally addresses environmental factors with a negative impact on human health. However, emerging scientific research has revealed a surprisingly positive and overlooked environmental factor on health: direct physical contact with the vast supply of electrons on the surface of the Earth. Modern lifestyle separates humans from such contact. The research suggests that this disconnect may be a major contributor to physiological dysfunction and unwellness. Reconnection with the Earth's electrons has been found to promote intriguing physiological changes and subjective reports of well-being. Earthing (or grounding) refers to the discovery of benefits-including better sleep and reduced pain-from walking barefoot outside or sitting, working, or sleeping indoors connected to conductive systems that transfer the Earth's electrons from the ground into the body. This paper reviews the earthing research and the potential of earthing as a simple and easily accessed global modality of significant clinical importance.

The representation of groundwater dynamics in landsurface models has received considerable attention in recent years. Most studies have found that soil moisture increases after adding a groundwater component because of the additional supply of water to the root zone. However, the effect of groundwater on landsurface hydrologic memory (persistence) has not been explored thoroughly. In this study we investigate the effect of water table dynamics on National Center for Atmospheric Research Community Land Model hydrologic simulations in terms of landsurface hydrologic memory. Unlike soil water or evapotranspiration, results show that landsurface hydrologic memory does not always increase after adding a groundwater component. In regions where the water table level is intermediate, landsurface hydrologic memory can even decrease, which occurs when soil moisture and capillary rise from groundwater are not in phase with each other. Further, we explore the hypothesis that in addition to atmospheric forcing, groundwater variations may also play an important role in affecting landsurface hydrologic memory. Analyses show that feedbacks of groundwater on landsurface hydrologic memory can be positive, negative, or neutral, depending on water table dynamics. In regions where the water table is shallow, the damping process of soil moisture variations by groundwater is not significant, and soil moisture variations are mostly controlled by random noise from atmospheric forcing. In contrast, in regions where the water table is very deep, capillary fluxes from groundwater are small, having limited potential to affect soil moisture variations. Therefore, a positive feedback of groundwater to landsurface hydrologic memory is observed in a transition zone between deep and shallow water tables, where capillary fluxes act as a buffer by reducing high-frequency soil moisture variations resulting in longer landsurface hydrologic memory.

In the beginning the surface of the Earth was extremely hot, because the Earth as we know it is the product of a collision between two planets, a collision that also created the Moon. Most of the heat within the very young Earth was lost quickly to space while the surface was still quite hot. As it cooled, the Earth's surface passed monotonically through every temperature regime between silicate vapor to liquid water and perhaps even to ice, eventually reaching an equilibrium with sunlight. Inevitably the surface passed through a time when the temperature was around 100°C at which modern thermophile organisms live. How long this warm epoch lasted depends on how long a thick greenhouse atmosphere can be maintained by heat flow from the Earth's interior, either directly as a supplement to insolation, or indirectly through its influence on the nascent carbonate cycle. In both cases, the duration of the warm epoch would have been controlled by processes within the Earth's interior where buffering by surface conditions played little part. A potentially evolutionarily significant warm period of between 105 and 107 years seems likely, which nonetheless was brief compared to the vast expanse of geological time. PMID:11259665

In the beginning the surface of the Earth was extremely hot, because the Earth as we know it is the product of a collision between two planets, a collision that also created the Moon. Most of the heat within the very young Earth was lost quickly to space while the surface was still quite hot. As it cooled, the Earth's surface passed monotonically through every temperature regime between silicate vapor to liquid water and perhaps even to ice, eventually reaching an equilibrium with sunlight. Inevitably the surface passed through a time when the temperature was around 100 degrees C at which modern thermophile organisms live. How long this warm epoch lasted depends on how long a thick greenhouse atmosphere can be maintained by heat flow from the Earth's interior, either directly as a supplement to insolation, or indirectly through its influence on the nascent carbonate cycle. In both cases, the duration of the warm epoch would have been controlled by processes within the Earth's interior where buffering by surface conditions played little part. A potentially evolutionarily significant warm period of between 10(5) and 10(7) years seems likely, which nonetheless was brief compared to the vast expanse of geological time.

The Western EarthSurface Processes Team (WESPT) of the U.S. Geological Survey (USGS) conducts geologic mapping and related topical earth science studies in the western United States. This work is focused on areas where modern geologic maps and associated earth-science data are needed to address key societal and environmental issues such as ground-water quality, landslides and other potential geologic hazards, and land-use decisions. Areas of primary emphasis in 2001 included southern California, the San Francisco Bay region, the Pacific Northwest, and the Las Vegas urban corridor. The team has its headquarters in Menlo Park, California, and maintains smaller field offices at several other locations in the western United States. The results of research conducted by the WESPT are released to the public as a variety of databases, maps, text reports, and abstracts, both through the internal publication system of the USGS and in diverse external publications such as scientific journals and books. This report lists publications of the WESPT released in 2002 as well as additional 1998 and 2001 publications that were not included in the previous list (USGS Open-File Report 00-215, USGS Open-File Report 01-198, and USGS Open-File Report 02-269). Most of the publications listed were authored or coauthored by WESPT staff. The list also includes some publications authored by non-USGS cooperators with the WESPT, as well as some authored by USGS staff outside the WESPT in cooperation with WESPT projects. Several of the publications listed are available on the World Wide Web; for these, URL addresses are provided. Many of these web publications are USGS open-file reports that contain large digital databases of geologic map and related information. Information on ordering USGS publications can be found on the World Wide Web or by calling 1-888-ASK-USGS. The U.S. Geological Survey’s web server for geologic information in the western United States is located at http

Mesoscale convective systems (MCSs) are important contributors to the hydrologic cycle in many regions of the world as well as major sources of severe weather. MCSs continue to challenge forecasters and researchers alike, arising from difficulties in understanding system initiation, propagation, and demise. One distinct type of MCS is that formed from individual convective cells initiated primarily by daytime heating over high terrain. This work is aimed at improving our understanding of the landsurface sensitivity of this class of MCS in the contiguous United States. First, a climatology of mesoscale convective systems originating in the Rocky Mountains and adjacent high plains from Wyoming southward to New Mexico is developed through a combination of objective and subjective methods. This class of MCS is most important, in terms of total warm season precipitation, in the 500 to 1300m elevations of the Great Plains (GP) to the east in eastern Colorado to central Nebraska and northwest Kansas. Examining MCSs by longevity, short lasting MCSs (15 hrs) reveals that longer lasting systems tend to form further south and have a longer track with a more southerly track. The environment into which the MCS is moving showed differences across commonly used variables in convection forecasting, with some variables showing more favorable conditions throughout (convective inhibition, 0-6 km shear and 250 hPa wind speed) ahead of longer lasting MCSs. Other variables, such as convective available potential energy, showed improving conditions through time for longer lasting MCSs. Some variables showed no difference across longevity of MCS (precipitable water and large-scale vertical motion). From subsets of this MCS climatology, three regions of origin were chosen based on the presence of ridgelines extending eastward from the Rocky Mountains known to be foci for convection initiation and subsequent MCS formation: Southern Wyoming (Cheyenne Ridge), Colorado (Palmer divide) and

Quantitative use of remote multispectral measurements to study and map landsurface evapotranspiration has been a challenging issue for the past 20 years. Past work is reviewed against process physics. A simple two-layer combination-type model is used which is applicable to both vegetation and bare soil. The theoretic analysis is done to show which landsurface properties are implicitly defined by such evaporation models and to assess whether they are measurable as a matter of principle. Conceptual implications of the spatial correlation of landsurface properties, as observed by means of remote multispectral measurements, are illustrated with results of work done in arid zones. A normalization of spatial variability of landsurface evaporation is proposed by defining a location-dependent potential evaporation and surface temperature range. Examples of the application of remote based estimates of evaporation to hydrological modeling studies in Egypt and Argentina are presented.

We present a new normal-mode formalism for computing the response of an aspherical, self-gravitating, linear viscoelastic earth model to an arbitrary surface load. The formalism makes use of recent advances in the theory of the Earth's free oscillations, and is based upon an eigenfunction expansion methodology, rather than the tradi-tional Love-number approach to surface-loading problems. We introduce a surface-load representation theorem analogous to Betti's reciprocity relation in seismology. Taking advantage of this theorem and the biorthogonality of the viscoelastic modes, we determine the complete response to a surface load in the form of a Green's function. We also demonstrate that each viscoelastic mode has its own unique energy partitioning, which can be used to characterize it. In subsequent papers, we apply the theory to spherically symmetric and aspherical earth models.

Google Earth (GE) is proving to be a valuable tool in the science classroom for understanding the environment and making responsible environmental decisions (Bodzin 2008). GE provides learners with a dynamic mapping experience using a simple interface with a limited range of functions. This interface makes geospatial analysis accessible and…

Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and landsurface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of landsurface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) landsurface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah landsurface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the landsurface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

A lidar instrument on a spacecraft was first used to measure planetary surface height and topography on the Apollo 15 mission to the Moon in 1971, The lidar was based around a flashlamp-pumped ruby laser, and the Apollo 15-17 missions used them to make a few thousand measurements of lunar surface height from orbit. With the advent of diode pumped lasers in the late 1980s, the lifetime, efficiency, resolution and mass of lasers and space lidar all improved dramatically. These advances were utilized in NASA space missions to map the shape and surface topography of Mars with > 600 million measurements, demonstrate initial space measurements of the Earth's topography, and measured the detailed shape of asteroid. NASA's ICESat mission in Earth orbit just completed its polar ice measurement mission with almost 2 billion measurements of the Earth's surface and atmosphere, and demonstrated measurements to Antarctica and Greenland with a height resolution of a few em. Space missions presently in cruise phase and in operation include those to Mercury and a topographic mapping mission of the Moon. Orbital lidar also have been used in experiments to demonstrate laser ranging over planetary distances, including laser pulse transmission from Earth to Mars orbit. Based on the demonstrated value of the measurements, lidar is now the preferred measurement approach for many new scientific space missions. Some missions planned by NASA include a planetary mission to measure the shape and dynamics of Europa, and several Earth orbiting missions to continue monitoring ice sheet heights, measure vegetation heights, assess atmospheric CO2 concentrations, and to map the Earthsurface topographic heights with 5 m spatial resolution. This presentation will give an overview of history, ongoing work, and plans for using space lidar for measurements of the surfaces of the Earth and planets.

Understanding surface modifications at landing site during spacecraft landing on planetary surfaces is important for planetary missions from scientific as well as engineering perspectives. An attempt has been made in this work to numerically investigate the disturbance caused to the lunar surface during soft landing. The variability of eject velocity of dust, eject mass flux rate, ejecta amount etc. has been studied. The effect of lander hovering time and hovering altitude on the extent of disturbance is also evaluated. The study thus carried out will help us in understanding the surface modifications during landing thereby making it easier to plan a descent trajectory that minimizes the extent of disturbance. The information about the extent of damage will also be helpful in interpreting the data obtained from experiments carried on the lunar surface in vicinity of the lander.

Earth's field NMR (EFNMR), being free of magnets, would be an ideal teaching medium as well as a mobile NMR technique except for its weak S/N. The common EFNMR apparatus uses a powerful prepolarization field to enhance the spin magnetization before the experiment. We introduce a coil design geared to larger but manageable samples with sufficient sensitivity without prepolarization to move EFNMR closer to routine use and to provide an inexpensive teaching tool. Our coil consists of parallel wires spread out on a plywood to form a current sheet with the current return wires separated so they will not influence the main part of the coil assembly. The sensitive region is a relatively thin region parallel to the coil and close to it. A single turn of the coil is wound to be topologically equivalent to a figure-8. The two crossing segments in the center of a figure-8 form two of the parallel wires of the flat coil. Thus, a two-turn figure-8 has four crossing wires so its topologically equivalent coil will have four parallel wires with currents in phase. Together with the excellent sensitivity, this coil offers outstanding interference rejection because of the figure-8 geometry. An example of such a coil has 328 parallel wires covering a ˜1 meter square plywood which yields a good NMR signal from 26 liters of water spread out roughly over the area of the coil in less than one minute in a nearby park.

Satellite observations have revolutionized the Earth Sciences and climate studies. However, data and imagery continue to accumulate at an accelerating rate, and efficient tools for data discovery, analysis, and quality checking lag behind. In particular, studies of long-term, continental-scale processes at high spatiotemporal resolutions are especially problematic. The traditional technique of downloading an entire dataset and using customized analysis code is often impractical or consumes too many resources. The Condensate Database Project was envisioned as an alternative method for data exploration and quality checking. The project's premise was that much of the data in any satellite dataset is unneeded and can be eliminated, compacting massive datasets into more manageable sizes. Dataset sizes are further reduced by retaining only anomalous data of high interest. Hosting the resulting "condensed" datasets in high-speed databases enables immediate availability for queries and exploration. Proof of the project's success relied on demonstrating that the anomaly database methods can enhance and accelerate scientific investigations. The hypothesis of this dissertation is that the condensed datasets are effective tools for exploring many scientific questions, spurring further investigations and revealing important information that might otherwise remain undetected. This dissertation uses condensed databases containing 17 years of Antarctic landsurface temperature anomalies as its primary data. The study demonstrates the utility of the condensate database methods by discovering new information. In particular, the process revealed critical quality problems in the source satellite data. The results are used as the starting point for four case studies, investigating Antarctic temperature extremes, cloud detection errors, and the teleconnections between Antarctic temperature anomalies and climate indices. The results confirm the hypothesis that the condensate databases

Two field experiments to study atmospheric and landsurface processes and their interactions are summarized. The Hydrologic-Atmospheric Pilot Experiment, which tested techniques for measuring evaporation, soil moisture storage, and runoff at scales of about 100 km, was conducted over a 100 X 100 km area in France from mid-1985 to early 1987. The first International Satellite LandSurface Climatology Program field experiment was conducted in 1987 to develop and use relationships between current satellite measurements and hydrologic, climatic, and biophysical variables at the earth's surface and to validate these relationships with ground truth. This experiment also validated surface parameterization methods for simulation models that describe surface processes from the scale of vegetation leaves up to scales appropriate to satellite remote sensing.

This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of landsurface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.

Significant land greening in the northern extratropical latitudes (NEL) has been documented from satellite observations during the past three decades. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales. Discernible human impacts on the Earth’s climate system have been revealed by using statistical frameworks of detection–attribution. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system modelsmore » (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We have used two 30-year-long remote-sensing-based leaf area index (LAI) data sets, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts.« less

The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-ofthe-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979-present. This study introduces a supplemental and improved set of landsurface hydrological fields ("MERRA-Land") generated by re-running a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in landsurface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim (ERA-I) reanalysis. MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 US basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for landsurface hydrological studies.

The landsurface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with landsurface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of landsurface-atmosphere interaction and the impact of landsurface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated landsurface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP to impose ad hoc corrections to the landsurface states to prevent this drift. A proposed methodology is to develop Land Data Assimilation schemes (LDAS), which are uncoupled models forced with observations, and not affected by NWP forcing biases. The proposed research is being implemented as a real time operation using an existing Surface Vegetation Atmosphere Transfer Scheme (SVATS) model at a 40 km degree resolution across the United States to evaluate these critical science questions. The model will be forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing GIS vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations.

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), provides global, 1-hourly estimates of landsurface conditions for 1980-present at 50-km resolution. Outside of the high latitudes, MERRA-2 uses observations-based precipitation data products to correct the precipitation falling on the landsurface. This paper describes the precipitation correction method and evaluates the MERRA-2 landsurface precipitation and hydrology. Compared to monthly GPCPv2.2 observations, the corrected MERRA-2 precipitation (M2CORR) is better than the precipitation generated by the atmospheric models within the cyclingMERRA-2 system and the earlier MERRA reanalysis. Compared to 3-hourlyTRMM observations, the M2CORR diurnal cycle has better amplitude but less realistic phasing than MERRA-2 model-generated precipitation. Because correcting the precipitation within the coupled atmosphere-land modeling system allows the MERRA-2 near-surface air temperature and humidity to respond to the improved precipitation forcing, MERRA-2 provides more self-consistent surface meteorological data than were available from the earlier, offline MERRA-Land reanalysis. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. The MERRA-2 and MERRA-Landsurface and root zone soil moisture skill vs. in situ measurements is slightly higher than that of ERA-Interim Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim Land. Seasonal anomaly R values against naturalized stream flow measurements in

Land and atmosphere interactions have long been recognized for playing a key role in climate and weather modeling. However their quantification has been challenging due to the complex nature of the landsurface amongst various other reasons. One of the difficult parts in the quantification is the effect of vegetation which are related to landsurface processes such soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/landsurface model, we examined the effects of vegetation and its relationship with soil moisture on the simulated land-atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah landsurface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Then, this study evaluates the model improvements for each simulation method.

Models of Earth's weather and climate require fluxes of momentum, energy, and moisture across the land-atmosphere interface to solve the equations of atmospheric physics and dynamics. Just as atmospheric models can, and do, differ between weather and climate applications, mostly related to issues of scale, resolved or parameterised physics,and computational requirements, so too can the land models that provide the required surface fluxes differ between weather and climate models. Here, however, the issue is less one of scale-dependent parameterisations.Computational demands can influence other minor land model differences, especially with respect to initialisation, data assimilation, and forecast skill. However, the distinction among land models (and their development and application) is largely driven by the different science and research needs of the weather and climate communities.

The Community Land Model (CLM) is the landsurface model used for the Community Atmosphere Model (CAM) and the Community Climate System Model (CCSM). It examines the physical, chemical, and biological processes across a variety of spatial and temporal scales. Currently, efforts are being made to improve the spatial resolution of the CLM, in part, to represent finer scale hydrologic characteristics. Current landsurface parameters of CLM4.0, in particular plant functional types (PFT) and leaf area index (LAI), are generated from MODIS and calculated at a 0.05 degree resolution. These MODIS-derived landsurface parameters have also been aggregated to coarser resolutions (e.g., 0.5, 1.0 degrees). To evaluate the response of CLM across various spatial scales, higher spatial resolution landsurface parameters need to be generated. In this study we examine the use of Landsat TM/ETM+ imagery and data fusion techniques for generating landsurface parameters at a 1km resolution within the Pacific Northwest United States. . Land cover types and PFTs are classified based on Landsat multi-season spectral information, DEM, National Land Cover Database (NLCD) and the USDA-NASS Crop Data Layer (CDL). For each PFT, relationships between MOD15A2 high quality LAI values, Landsat-based vegetation indices, climate variables, terrain, and laser-altimeter derived vegetation height are used to generate monthly LAI values at a 30m resolution. The high-resolution PFT and LAI data are aggregated to create a 1km model grid resolution. An evaluation and comparison of CLM landsurface response at both fine and moderate scale is presented.

pressure gradients, and modeled winds from weather re-analyses do not exhibit any comparable stilling trends than at surface stations. For instance, large-scale circulation changes captured in the most recent European Centre for Medium Range Weather Forecast re-analysis (ERA-interim) can only explain only up to 10-50% of the wind stilling, depending on the region. In addition, a significant amount of the slow-down could originate from a generalized increase in surface roughness, due for instance to forest growth and expansion, and urbanization. This hypothesis, which could explain up to 60% of the decline, is supported by remote sensing observations and theoretical calculations combined with meso-scale model simulations. For future wind power energy resource, the part of wind decline due to land cover changes is easier to cope with than that due to global atmospheric circulation slow down.

Infrared and radar studies of the Apollo 16 landing site are summarized. Correlations and comparisons between earth based remote sensing data, IR observations, and other data are discussed in detail. Remote sensing studies were devoted to solving two problems: (1) determining the physical difference between Cayley and Descartes geologic units near the landing site; and (2) determining the nature of the bright unit of Descartes mountain material.

One is likely to read the terms 'land use' and 'land cover' in the same sentence, yet these concepts have different origins and different applications. Land cover is typically analyzed by earth scientists working with remotely sensed images. Land use is typically studied by urban planners who must prescribe solutions that could prevent future problems. This apparent dichotomy has led to different classification systems for land-based data. The works of earth scientists and urban planning practitioners are beginning to come together in the field of spatial analysis and in their common use of new spatial analysis technology. In this context, the technology can stimulate a common 'language' that allows a broader sharing of ideas. The increasing amount of land use and land cover change challenges the various efforts to classify in ways that are efficient, effective, and agreeable to all groups of users. If land cover and land uses can be identified by remote methods using aerial photography and satellites, then these ways are more efficient than field surveys of the same area. New technology, such as high-resolution satellite sensors, and new methods, such as more refined algorithms for image interpretation, are providing refined data to better identify the actual cover and apparent use of land, thus effectiveness is improved. However, the closer together and the more vertical the land uses are, the more difficult the task of identification is, and the greater is the need to supplement remotely sensed data with field study (in situ). Thus, a number of land classification methods were developed in order to organize the greatly expanding volume of data on land characteristics in ways useful to different groups. This paper distinguishes two land based classification systems, one developed primarily for remotely sensed data, and the other, a more comprehensive system requiring in situ collection methods. The intent is to look at how the two systems developed and how they

Earlier studies have shown that an earth-orbiting electrically scanned microwave radiometer (ESMR) is capable of inferring the extent, concentration, and age of sea ice; the extent, concentration, and thickness of lake ice; rainfall rates over oceans; surface wind speeds over open water; particle size distribution in the deep snow cover of continental ice sheets; and soil moisture content in unvegetated fields. Most other features of the surface of the earth and its atmosphere require multispectral imaging techniques to unscramble the combined contributions of the atmosphere and the surface. Multispectral extraction of surface parameters is analyzed on the basis of a pertinent equation in terms of the observed brightness temperature, the emissivity of the surface which depends on wavelength and various parameters, the sensible temperature of the surface, and the total atmospheric opacity which is also wavelength dependent. Implementation of the multispectral technique is examined. Properties of the surface of the earth and its atmosphere to be determined from a scanning multichannel microwave radiometer are tabulated.

It is shown that the greenhouse gases carbon dioxide and water vapour reflect back to the surface, all IR radiation originating at the surface within their respective spectral bands. This reflection occurs in a very thin layer at the surface, not much over 12 cm in thickness. Heat is lost from the surface by heat exchange with the atmosphere and by loss of radiation. About 52% of radiation leaves the surface in two principal window regions but this is not enough to account for the earth's equilibrium temperature. This window radiation seems to disappear quite quickly and is replaced by black body radiation. It is this which eventually contributes to the earth's radiation balance, and has to originate approximately between 40 and 50 km altitude where the temperature is about correct, near 255 K. Doubling the CO2 concentration increases the surface temperature by about 0.9 °C and this need not have any influence higher up in the atmosphere. The surface temperature seems indeed to have no direct influence on the earth's external radiation balance.

This paper describes NASA's initial steps for identifying and evaluating candidate Exploration Zones (EZs) and Regions of Interests (ROIs) for the first human crews that will explore the surface of Mars. NASA's current effort to define the exploration of this planet by human crews, known as the Evolvable Mars Campaign (EMC), provides the context in which these EZs and ROIs are being considered. The EMC spans all aspects of a human Mars mission including launch from Earth, transit to and from Mars, and operations on the surface of Mars. An EZ is a collection of ROIs located within approximately 100 kilometers of a centralized landing site. ROIs are areas relevant for scientific investigation and/or development/maturation of capabilities and resources necessary for a sustainable human presence. The EZ also contains one or more landing sites and a habitation site that will be used by multiple human crews during missions to explore and utilize the ROIs within the EZ. With the EMC as a conceptual basis, the EZ model has been refined to a point where specific site selection criteria for scientific exploration and in situ resource utilization can be defined. In 2015 these criteria were distributed to the planetary sciences community and the in situ resource utilization and civil engineering communities as part of a call for EZ proposals. The resulting "First Landing Site/Exploration Zone Workshop for Human Missions to the Surface of Mars" was held in October 2015 during which 47 proposals for EZs and ROIs were presented and discussed. Proposed locations spanned all longitudes and all allowable latitudes (+/- 50 degrees). Proposed justification for selecting one of these EZs also spanned a significant portion of the scientific and resource criteria provided to the community. Several important findings resulted from this Workshop including: (a) a strong consensus that, at a scale of 100 km (radius), multiple places on Mars exist that have both sufficient scientific interest

Land subsidence due to groundwater overdraft has been an ongoing problem in south-central and southern Arizona (USA) since the 1940s. The first earth fissure attributed to excessive groundwater withdrawal was discovered in the early 1950s near Picacho. In some areas of the state, groundwater-level declines of more than 150 m have resulted in extensive land subsidence and earth fissuring. Land subsidence in excess of 5.7 m has been documented in both western metropolitan Phoenix and Eloy. The Arizona Department of Water Resources (ADWR) has been monitoring land subsidence since 2002 using interferometric synthetic aperture radar (InSAR) and since 1998 using a global navigation satellite system (GNSS). The ADWR InSAR program has identified more than 25 individual land subsidence features that cover an area of more than 7,300 km2. Using InSAR data in conjunction with groundwater-level datasets, ADWR is able to monitor land subsidence areas as well as identify areas that may require additional monitoring. One area of particular concern is the Willcox groundwater basin in southeastern Arizona, which is the focus of this paper. The area is experiencing rapid groundwater declines, as much as 32.1 m during 2005-2014 (the largest land subsidence rate in Arizona State—up to 12 cm/year), and a large number of earth fissures. The declining groundwater levels in Arizona are a challenge for both future groundwater availability and mitigating land subsidence associated with these declines. ADWR's InSAR program will continue to be a critical tool for monitoring land subsidence due to excessive groundwater withdrawal.

A landsurface hydrology parameterization for use in atmospheric GCM's is presented. The parameterization incorporates subgrid scale variability in topography, soils, soil moisture and precipitation. The framework of the model is the statistical distribution of a topography-soils index, which controls the local water balance fluxes, and is therefore taken to represent the large land area. Spatially variable water balance fluxes are integrated with respect to the topography-soils index to yield our large topography-soils distribution, and interval responses are weighted by the probability of occurrence of the interval. Grid square averaged landsurface fluxes result. The model functions independently as a macroscale water balance model. Runoff ratio and evapotranspiration efficiency parameterizations are derived and are shown to depend on the spatial variability of the above mentioned properties and processes, as well as the dynamics of landsurface-atmosphere interactions.

The surfaces of the earth and the other terrestrial planets of the inner solar system are reviewed in light of the results of recent planetary explorations. Past and current views of the origin of the earth, moon, Mercury, Venus and Mars are discussed, and the surface features characteristic of the moon, Mercury, Mars and Venus are outlined. Mechanisms for the modification of planetary surfaces by external factors and from within the planet are examined, including surface cycles, meteoritic impact, gravity, wind, plate tectonics, volcanism and crustal deformation. The origin and evolution of the moon are discussed on the basis of the Apollo results, and current knowledge of Mercury and Mars is examined in detail. Finally, the middle periods in the history of the terrestrial planets are compared, and future prospects for the exploration of the inner planets as well as other rocky bodies in the solar system are discussed.

On Sept. 16, 2015, a magnitude 8.3 earthquake struck near the coast of central Chile along the boundary of the Nazca and South American tectonic plates. Dubbed the Illapel earthquake, the shaking lasted at least three minutes and propelled a 15-foot (4.5-meter) tsunami that washed into Coquimbo and other coastal areas. Smaller tsunami waves raced across the Pacific and showed up on the shores of Hawaii and other islands. The earthquake and tsunami caused substantial damage in several Chilean coastal towns, and at least 13 deaths have been reported. Demanding building codes and extensive disaster preparedness helped to limit the loss of life and property. The maps above, known as interferograms, show how the quake moved the ground, as observed by the Copernicus Sentinel-1A satellite (operated by the European Space Agency) and reported by ground stations to the U.S. Geological Survey. Sentinel-1A carries a synthetic aperture radar (SAR) instrument, which beams radio signals toward the ground and measures the reflections to determine the distance between the ground and the satellite. By comparing measurements made on Aug. 24 and Sept. 17, Cunren Liang, Eric Fielding, and other researchers from NASA's Jet Propulsion Laboratory were able to determine how the landsurface shifted during and after the earthquake. Interferograms can be used to estimate where the fault moved deep in Earth and which areas have increased stress and higher likelihood of future earthquakes. The details can also provide important information to better understand the earthquake process. On both the close-up and the broad-view maps, the amount of land motion is represented in shades from yellow to purple. Areas where the ground shifted the most (vertically, horizontally, or both) are represented in yellow, while areas with little change are represented in purple. Circles show the location of earthquakes and aftershocks in the two days after the initial 8.3 earthquake, as reported by the USGS

Liquid water is one of the most important materials affecting the climate and habitability of a terrestrial planet. Liquid water vaporizes entirely when planets receive insolation above a certain critical value, which is called the runaway greenhouse threshold. This threshold forms the inner most limit of the habitable zone. Here we investigate the effects of the distribution of surface water on the runaway greenhouse threshold for Earth-sized planets using a three-dimensional dynamic atmosphere model. We considered a 1 bar atmosphere whose composition is similar to the current Earth's atmosphere with a zonally uniform distribution of surface water. As previous studies have already showed, we also recognized two climate regimes: the land planet regime, which has dry low-latitude and wet high-latitude regions, and the aqua planet regime, which is globally wet. We showed that each regime is controlled by the width of the Hadley circulation, the amount of surface water, and the planetary topography. We found that the runaway greenhouse threshold varies continuously with the surface water distribution from about 130% (an aqua planet) to 180% (the extreme case of a land planet) of the present insolation at Earth's orbit. Our results indicate that the inner edge of the habitable zone is not a single sharp boundary, but a border whose location varies depending on planetary surface condition, such as the amount of surface water. Since land planets have wider habitable zones and less cloud cover, land planets would be good targets for future observations investigating planetary habitability.

Landsurface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Being a key boundary condition in landsurface models, which determine the surface to atmosphere fluxes of heat, water and carbon; thus influencing cloud cover, precipitation and atmospheric chemistry predictions within Global models, the requirement for global diurnal observations of LST is well founded. Earth Observation satellites offer an opportunity to obtain global coverage of LST, with the appropriate exploitation of data from multiple instruments providing a capacity to resolve the diurnal cycle on a global scale. Here we present a framework for the production of global, diurnally resolved, data sets for LST which is a key request from users of LST data. We will show how the sampling of both geostationary and low earth orbit data sets could conceptually be employed to build combined, multi-sensor, pole-to-pole data sets. Although global averages already exist for individual instruments and merging of geostationary based LST is already being addressed operationally (Freitas, et al., 2013), there are still a number of important challenges to overcome. In this presentation, we will consider three of the issues still open in LST remote sensing: 1) the consistency amongst retrievals; 2) the clear-sky bias and its quantification; and 3) merging methods and the propagation of uncertainties. For example, the combined use of both geostationary earth orbit (GEO) and low earth orbit (LEO) data, and both infra-red and microwave data are relatively unexplored but are necessary to make the most progress. Hence this study will suggest what is state-of-the-art and how considerable advances can be made, accounting also for recent improvements in techniques and data quality. The GlobTemperature initiative under the Data User Element of ESA's 4th Earth Observation Envelope Programme (2013

Landsurface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Being a key boundary condition in landsurface models, which determine the surface to atmosphere fluxes of heat, water and carbon; thus influencing cloud cover, precipitation and atmospheric chemistry predictions within Global models, the requirement for global diurnal observations of LST is well founded. Earth Observation satellites offer an opportunity to obtain global coverage of LST, with the appropriate exploitation of data from multiple instruments providing a capacity to resolve the diurnal cycle on a global scale. Here we present a framework for the production of global, diurnally resolved, data sets for LST which is a key request from users of LST data. We will show how the sampling of both geostationary and low earth orbit data sets could conceptually be employed to build combined, multi-sensor, pole-to-pole data sets. Although global averages already exist for individual instruments and merging of geostationary based LST is already being addressed operationally (Freitas, et al., 2013), there are still a number of important challenges to overcome. In this presentation, we will consider three of the issues still open in LST remote sensing: 1) the consistency amongst retrievals; 2) the clear-sky bias and its quantification; and 3) merging methods and the propagation of uncertainties. For example, the combined use of both geostationary earth orbit (GEO) and low earth orbit (LEO) data, and both infra-red and microwave data are relatively unexplored but are necessary to make the most progress. Hence this study will suggest what is state-of-the-art and how considerable advances can be made, accounting also for recent improvements in techniques and data quality. The GlobTemperature initiative under the Data User Element of ESA's 4th Earth Observation Envelope Programme (2013

Surface net radiation, which characterizes surface energy budget, can be estimated from in-situ measurements, satellite products, model simulations, and reanalysis. Satellite products are usually validated using ground measurements to characterize their uncertainties. The surface net radiation product from the CERES (Clouds and the Earth's Radiant Energy System) has been widely used. After validating it using extensive ground measurements, we also verified that the CERES surface net radiation product is highly accurate. When we evaluated the temporal variations of the averaged global landsurface net radiation from the CERES product, we found a significantly negative anomaly starting from 2001, reaching the maximum in 2004, and gradually coming back to normal in 2006. The valley has the magnitude of approximately 3 Wm-2 centered at 2004. After comparing with the high-resolution GLASS (Global LAndSurface Satellite) net radiation product developed at Beijing Normal University, the CMIP5 model simulations, and the ERA-Interim reanalysis dataset, we concluded that the significant decreasing pattern of landsurface net radiation from 2001-2006 is an artifact mainly due to inaccurate longwave net radiation of the CERES surface net radiation product. The current ground measurement networks are not spatially dense enough to capture the false negative anomaly from the CERES product, which calls for more ground measurements.

A key element of the President's Vision for Space Exploration is the development of a new space transportation system to replace the Shuttle that will enable manned exploration of the moon, Mars, and beyond. NASA has tasked the Constellation Program with the development of this architecture, which includes the Ares launch vehicle and Orion manned spacecraft. The Orion spacecraft must carry six astronauts and its primary structure should be reusable, if practical. These requirements led the Constellation Program to consider a baseline landlanding on return to earth. To assess the landing system options for Orion, a review of current operational parachute landing systems such as those used for the F-111 escape module and the Soyuz is performed. In particular, landing systems with airbags and retrorockets that would enable reusability of the Orion capsule are investigated. In addition, Apollo tests and analyses conducted in the 1960's for both water and landlandings are reviewed. Finally, tests and dynamic finite element simulations to understand landlandings for the Orion spacecraft are also presented.

A key element of the President's Vision for Space Exploration is the development of a new space transportation system to replace Shuttle that will enable manned exploration of the moon, Mars, and beyond. NASA has tasked the Constellation Program with the development of this architecture, which includes the Ares launch vehicle and Orion manned spacecraft. The Orion spacecraft must carry six astronauts and its primary structure should be reusable, if practical. These requirements led the Constellation Program to consider a baseline landlanding on return to earth. To assess the landing system options for Orion, a review of current operational parachute landing systems such as those used for the F-111 escape module and the Soyuz is performed. In particular, landing systems with airbags and retrorockets that would enable reusability of the Orion capsule are investigated. In addition, Apollo tests and analyses conducted in the 1960's for both water and landlandings are reviewed. Finally, tests and dynamic finite element simulations to understand landlandings for the Orion spacecraft are also presented.

Since launching in April 2006 the main objective of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission has been studying the climate impact of clouds and aerosols in the atmosphere. However, CALIPSO also collects information about other components of the Earth's ecosystem, such as lands, oceans and polar ice sheets. The objective of this study is to propose a Super-Resolution Altimetry (SRA) technique to provide high resolution of land, ocean and polar ice sheet surface elevation from CALIPSO single shot lidar measurements (70 m spot size). The landsurface results by the new technique agree with the United States Geological Survey (USGS) National Elevation Database (NED) high-resolution elevation maps, and the ice sheet surface results in the region of Greenland and Antarctic compare very well with the Ice, Cloud and land Elevation Satellite (ICESat) laser altimetry measurements. The comparisons suggest that the obtained CALIPSO surface elevation information by the new technique is accurate to within 1 m. The effects of error sources on the retrieved surface elevation are discussed. Based on the new technique, the preliminary data products of along-track topography retrieved from the CALIPSO lidar measurements is available to the altimetry community for evaluation.

Soil moisture plays a central role in the partition of available energy at the landsurface between sensible and latent heat flux to the atmosphere. As soils dry out, evapotranspiration becomes water-limited ("stressed"), and both landsurface temperature (LST) and sensible heat flux rise as a result. This change in surface behaviour during dry spells directly affects critical processes in both the land and the atmosphere. Soil water deficits are often a precursor in heat waves, and they control where feedbacks on precipitation become significant. State-of-the-art global climate model (GCM) simulations for the Coupled Model Intercomparison Project Phase 5 (CMIP5) disagree on where and how strongly the surface energy budget is limited by soil moisture. Evaluation of GCM simulations at global scale is still a major challenge owing to the scarcity and uncertainty of observational datasets of landsurface fluxes and soil moisture at the appropriate scale. Earth observation offers the potential to test how well GCM land schemes simulate hydrological controls on surface fluxes. In particular, satellite observations of LST provide indirect information about the surface energy partition at 1km resolution globally. Here, we present a potentially powerful methodology to evaluate soil moisture stress on surface fluxes within GCMs. Our diagnostic, Relative Warming Rate (RWR), is a measure of how rapidly the land warms relative to the overlying atmosphere during dry spells lasting at least 10 days. Under clear skies, this is a proxy for the change in sensible heat flux as soil dries out. We derived RWR from MODIS Terra and Aqua LST observations, meteorological re-analyses and satellite rainfall datasets. Globally we found that on average, the land warmed up during dry spells for 97% of the observed surface between 60S and 60N. For 73% of the area, the land warmed faster than the atmosphere (positive RWR), indicating water stressed conditions and increases in sensible heat flux

Because landsurface emissivity (ɛ) has not been reliably measured, global climate model (GCM) landsurface schemes conventionally set this parameter as simply assumption, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, 0.96 for soil and wetland in the Global and Regional Assimilation and Prediction System (GRAPES) Common Land Model (CoLM). This is the so-called emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the landsurface climate. It is demonstrated in this paper that the assumption of the emissivity induces errors in modeling the surface energy budget over Taklimakan Desert where ɛ is far smaller than original value. One feasible solution to this problem is to apply the accurate broadband emissivity into landsurface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity required by landsurface models. In order to calibrate the regression equations, using a portable Fourier Transform infrared (FTIR) spectrometer instrument, crossing Taklimakan Desert along with highway from north to south, to measure the accurate broadband emissivity. The observed emissivity data show broadband ɛ around 0.89-0.92. To examine the impact of improved ɛ to radiative energy redistribution, simulation studies were conducted using offline CoLM. The results illustrate that large impacts of surface ɛ occur over desert, with changes up in surface skin temperature, as well as evident changes in sensible heat fluxes. Keywords: Taklimakan Desert, surface broadband emissivity, Fourier Transform infrared spectrometer, MODIS, CoLM

Earth's surface loading/unloading such as glacier retreat, lake water level change, ocean tide, cause measurable (centimeter to millimeter) surface deformation from Synthetic Aperture Radar Interferometry (InSAR). Such seasonal or decadal deformation signals are useful for the estimation of the amount of load and the parameterization of crust and upper mantle - typically under an elastic or a visco-elastic mechanism. Since 2010, we established a study of surface loading using small baseline InSAR time-series analysis. Four sites are included in this study, which are Vatnajokull ice cap, Lake Yamzho Yumco, Petermann glacier, and Barnes ice cap using different satellites such as ERS1/2, Envisat, Radarsat-2, TerraSAR-X. We present results that mainly answer three questions: 1) Is InSAR time-series capable for the detection of millimeter level deformation due to surface loading; 2) When the Earth's rheology is known, how much load change occured; 3) When the surface loading is known, what are the Earth's parameters such as Young's modulus, viscosity. For glacier retreat problem, we introduce a new model for the ice mass loss estimation considering the spatial distribution of ice loss. For lake unloading problem, modeled elastic parameters are useful for the comparison to other 1-D models, e.g. the model based on seismic data.

The formation, atomic structure, and electronic properties of self-assembled rare-earth silicide nanowires on silicon surfaces were studied by scanning tunneling microscopy and angle-resolved photoelectron spectroscopy. Metallic dysprosium and erbium silicide nanowires were observed on both the Si(001) and Si(557) surfaces. It was found that they consist of hexagonal rare-earth disilicides for both surface orientations. On Si(001), the nanowires are characterized by a one-dimensional band structure, while the electronic dispersion is two-dimensional for the nanowires formed on Si(557). This behavior is explained by the different orientations of the hexagonal c axis of the silicide leading to different conditions for the carrier confinement. By considering this carrier confinement it is demonstrated how the one-dimensional band structure of the nanowires on Si(001) can be derived from the two-dimensional one of the silicide monolayer on Si(111).

Large volumes of MODIS land data products at multiple spatial resolutions have been integrated into the Giovanni online analysis system to support studies on land cover and land use changes,focused on the Northern Eurasia and Monsoon Asia regions through the LCLUC program. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data.Customized Giovanni Web portals (Giovanni-NEESPI andGiovanni-MAIRS) have been created to integrate land, atmospheric,cryospheric, and societal products, enabling researchers to do quick exploration and basic analyses of landsurface changes, and their relationships to climate, at global and regional scales. This presentation shows a sample Giovanni portal page, lists selected data products in the system, and illustrates potential analyses with imagesand time-series at global and regional scales, focusing on climatology and anomaly analysis. More information is available at the GES DISCMAIRS data support project portal: http:disc.sci.gsfc.nasa.govmairs.

... DEPARTMENT OF STATE [Public Notice 7877] Culturally Significant Objects Imported for Exhibition Determinations: ``Ends of the Earth: Land Art to 1974'' SUMMARY: Notice is hereby given of the following... ``Ends of the Earth: Land Art to 1974'' imported from abroad for temporary exhibition within the United...

The Northern Great Plains of the US have been undergoing many types of land cover / land use change over the past two decades, including expansion of irrigation, conversion of grassland to cropland, biofuels production, urbanization, and fossil fuel mining. Much of the literature on these changes has relied on post-classification change detection based on a limited number of observations per year. Here we demonstrate an approach to characterize land dynamics through landsurface phenology (LSP) by synergistic use of image time series at two scales. Our study areas include regions of interest (ROIs) across the Northern Great Plains located within Landsat path overlap zones to boost the number of valid observations (free of clouds or snow) each year. We first compute accumulated growing degree-days (AGDD) from MODIS 8-day composites of landsurface temperature (MOD11A2 and MYD11A2). Using Landsat Collection 1 surface reflectance-derived vegetation indices (NDVI, EVI), we then fit at each pixel a downward convex quadratic model linking the vegetation index to each year's progression of AGDD. This quadratic equation exhibits linearity in a mathematical sense; thus, the fitted models can be linearly mixed and unmixed using a set of LSP endmembers (defined by the fitted parameter coefficients of the quadratic model) that represent "pure" land cover types with distinct seasonal patterns found within the region, such as winter wheat, spring wheat, maize, soybean, sunflower, hay/pasture/grassland, developed/built-up, among others. Information about land cover corresponding to each endmember are provided by the NLCD (National Land Cover Dataset) and CDL (Cropland Data Layer). We use linear unmixing to estimate the likely proportion of each LSP endmember within particular areas stratified by latitude. By tracking the proportions over the 2001-2011 period, we can quantify various types of land transitions in the Northern Great Plains.

This talk discusses smoke and aerosol's radiative properties with particular attention to distinguishing the measurement over clear sky from clouds over land, sea, snow, etc. surfaces, using MODIS Airborne Simulator data from (Brazil, arctic sea ice and tundra and southern Africa, west Africa, and other ecosystems. This talk also discusses the surface bidirectional reflectance using Cloud Absorption Radiometer, BRDF measurements of Saudi Arabian desert, Persian Gulf, cerrado and rain forests in Brazil, sea ice, tundra, Atlantic Ocean, Great Dismal Swamp, Kuwait oil fire smoke. Recent upgrades to instrument (new TOMS UVA channels at 340 and 380 planned use in Africa (SAFARI 2000) and possibly for MEIDEX will also be discussed. This talk also plans to discuss the spectral variation of surface reflectance over land and the sensitivity of off-nadir view angles to correlation between visible near-infrared reflectance for use in remote sensing of aerosol over land.

Landsurface parameters may affect local microclimate, which in turn alters the development of mosquito habitats and transmission risks (soil-vegetation-atmosphere-vector borne diseases). Forest malaria is a chromic issue in Southeast Asian countries, in particular, such as Vietnam (in 1991, approximate 2 million cases and 4,646 deaths were reported (https://sites.path.org)). Vietnam has lowlands, sub-tropical high humidity, and dense forests, resulting in wide-scale distribution and high biting rate of mosquitos in Vietnam, becoming a challenging and out of control scenario, especially in Vietnamese Central Highland region. It is known that Vietnam's economy mainly relies on agriculture and malaria is commonly associated with poverty. There is a strong demand to investigate the relationship between landsurface parameters (land cover, soil moisture, landsurface temperature, etc.) and climatic variables (precipitation, humidity, evapotranspiration, etc.) in association with malaria distribution. GIS and remote sensing have been proven their powerful potentials in supporting environmental and health studies. The objective of this study aims to analyze physical attributes of landsurface and climate parameters and their links with malaria features. The outcomes are expected to illustrate how remotely sensed data has been utilized in geohealth applications, surveillance, and health risk mapping. In addition, a platform with promising possibilities of allowing disease early-warning systems with citizen participation will be proposed.

Irrigation is the main water user in the world with 87 % of the global water consumption being attributed to use on irrigated crop land. There are large spatial variations of the irrigated areas, from 68 % in Asia and 16 % in America, 10 % in Europe and the remaining in Africa and Australia. India is the most important irrigating country in the world with a gross irrigation requirement estimated by the FAO at 457 cubic km by year. The environmental impacts of irrigation are very important: irrigation causes the soil salinization, it affects the water quality and ecology, and increases the incidence of water related diseases. Irrigation is also expected to affect the the landsurface energy budget, and thereby the climate system. The work presented here is conducted in the framework of the PROMISE European project. It aims to analyze the sensitivity of the landsurface fluxes to the intensive irrigation over Indian peninsula. Numerical experiments are conducted with the landsurface scheme ORCHIDEE of the Laboratoire de Meteorologie Dynamique, with a 1 degree spatial resolution. Two 2years simulations, forced by the ISLSCP (1987-88) data sets, are compared, with and without irrigation. The analysis focuses on the effect of land irrigation on the surface fluxes (partition of energy between latent and sensible fluxes), and the river flow.

This slide presentation reviews how our understanding of the water cycle is enhanced by our use of satellite data, and how this informs landsurface hydrology and water resource management. It reviews how NASA's current and future satellite missions will provide Earth system data of unprecedented breadth, accuracy and utility for hydrologic analysis.

Landsurface albedo is a significant parameter for maintaining a balance in surface energy. It is also an important parameter of bare soil surface albedo for developing landsurface process models that accurately reflect diurnal variation characteristics and the mechanism behind the solar spectral radiation albedo on bare soil surfaces and for understanding the relationships between climate factors and spectral radiation albedo. Using a data set of field observations, we conducted experiments to analyze the variation characteristics of landsurface solar spectral radiation and the corresponding albedo over a typical Gobi bare soil underlying surface and to investigate the relationships between the landsurface solar spectral radiation albedo, solar elevation angle, and soil moisture. Based on both solar elevation angle and soil moisture measurements simultaneously, we propose a new two-factor parameterization scheme for spectral radiation albedo over bare soil underlying surfaces. The results of numerical simulation experiments show that the new parameterization scheme can more accurately depict the diurnal variation characteristics of bare soil surface albedo than the previous schemes. Solar elevation angle is one of the most important factors for parameterizing bare soil surface albedo and must be considered in the parameterization scheme, especially in arid and semiarid areas with low soil moisture content. This study reveals the characteristics and mechanism of the diurnal variation of bare soil surface solar spectral radiation albedo and is helpful in developing landsurface process models, weather models, and climate models.

EarthShape - "Earthsurface shaping by biota" is a 6-year priority research program funded by the German science foundation (DFG-SPP 1803) that performs soil- and landscape-scale critical zone research at 4 locations along a climate gradient in the Chilean Coastal Cordillera. This region was selected because of its north-south orientation such that it captures a large ecological and climate gradient ranging from hyper-arid to temperate to humid conditions. The sites comprise granitic, previously unglaciated mountain ranges. EarthShape involves an interdisciplinary collaboration between geologists, geomorphologists, ecologists, soil scientists, microbiologists, geophysicists, geochemists, and hydrogeologists including 18 German and 8 Chilean institutions. EarthShape is composed of 4 research clusters representing the process chain from weathering of substrate to deposition of eroded material. Cluster 1 explores micro-biota as the "weathering engine". Investigations in this cluster quantify different mechanisms of biogenic weathering whereby plants, fungi, and bacteria interact with rock in the production of soil. Cluster 2 explores bio-mediated redistribution of material within the weathering zone. Studies in this cluster focus on soil catenas along hill slope profiles to investigate the modification of matter along its transport path. Cluster 3 explores biotic modulation of erosion and sediment routing at the catchment scale. Investigations in this cluster explore the effects of vegetation cover on solute and sediment transport from hill slopes to the channel network. Cluster 4 explores the depositional legacy of coupled biogenic and Earthsurface systems. This cluster investigates records of vegetation-landsurface interactions in different depositional settings. A final component of EarthShape lies in the integration of results from these 4 clusters using numerical models to bridging between the diverse times scales used by different disciplines.

One of the main tasks of a glaciologist is to determine the mass budget of a glacier; and snow accumulation is the first part of the equation. To do this, a great number of snow pits must be dug to analyze the stratigraphy. A. P. Crary once said “to be a glaciologist one should first of all love to dig snow pits.” Seventy-five snow pits were dug on the SPQMLT traverses. Several experienced glaciologists had difficulty in interpreting the stratigraphic sequences in these pits. Irregular layering, caused by uneven deposition and subsequent erosion, suggested that some of the layers could be missing. However, the fallout of artificial radioactive nuclides released by the first large thermonuclear bomb test, on March 1, 1954, at Castle Bravo on Bikini Atoll, Marshall Islands, produced a datum horizon over the Antarctic ice sheet. This horizon is the summer of 1954-55 and provides the basis for measuring the average accumulation since 1955. Accumulation varied from 6.7 ± 0.2 g cm-2 yr-1 at South Pole Station to a low of 0.6 ± 0.2 g cm-2 yr-1 in a pit on the second leg of SPQMLT 2. The average accumulation along the entire traverse route was 3.7 g cm-2 yr-1. Temperatures at ten meters (considered an approximate mean annual air temperature) varied from -58.4 degrees Centigrade at Plateau Station (elevation 3620 meters) to -38 degrees Centigrade at the terminus of SPQMLT 3 (elevation 2310 meters). The condition of the ice sheet surface varied considerably. Some surface was quite hard and easy to traverse; while other areas that were smooth and soft were troublesome enough to bog down vehicles and sleds. Sastrugi were sporadic with some as high as a meter. A large crevasse field forced a slight change in course toward the end of the first leg of SPQMLT 2. There the ice thickness changed dramatically from 3060 meters to 1852 meters. At the time the geophysicist said, “The ‘bottom’ came up so fast I thought we would hit a nunatak.”

Weathering and erosion of rock and the transport of sediment continually modify Earth's surface. The transformation and transfer of material by both natural and anthropogenic processes drives global cycles and influences the habitability of our planet. By quantitatively linking erosional and depositional landforms to the processes that form them, we better understand how Earth's surface will evolve in the future, and gain the ability to look into the past to recognize how planetary surfaces evolved when environments were drastically different than today. Many of the recent advances in our understanding of the processes that influence landscape evolution have been driven by the development and application of tools such as cosmogenic nuclides, computational models, and digital topographic data. Here I present results gleaned from applying these tools to a diverse set of landscapes, where erosion is driven by factors ranging from tectonics to tractors, to provide insight into the mechanics, chemistry, and history of Earth's changing surface. I will first examine the landslide response of hillslopes in the Himalaya to spatial gradients in tectonic forcing to assess the paradigm of threshold hillslopes. Second, I will present soil production and chemical weathering rates measured in the Southern Alps of New Zealand to determine the relationship between physical erosion and chemical weathering in one of Earth's most rapidly uplifting landscapes, and discuss the implications for proposed links between mountain uplift and global climate. Third, I will discuss results from numerical flood simulations used to explore the interplay between outburst flood hydraulics and canyon incision in the Channeled Scablands of eastern Washington, and explore the implications for reconstructing discharge in flood-carved canyons on Earth and Mars. Finally, I will present new work that couples high resolution spectral and topographic data to estimate the spatial extent of agriculturally

Prior studies have demonstrated that habitable areas on low-obliquity land planets are confined to the edges of frozen ice caps. Whether such dry planets can maintain long-lived liquid water is unclear. Leconte et al. 2013 argue that on such planets mechanisms like gravity driven ice flows and geothermal flux can maintain liquid water at the edges of thick ice caps and this water may flow back to the lower latitudes through rivers. However, there exists no modelling study which investigates the climate of an Earth-like land planet with an overland recycling mechanism bringing fresh water back from higher to lower latitudes. In our study, by using a comprehensive climate model ICON, we find that an Earth-like land planet with an overland recycling mechanism can exist in two drastically different climate states for the same set of boundary conditions and parameter values: A Cold and Wet (CW) state with dominant low-latitude precipitation and, a Hot and Dry (HD) state with only high-latitude precipitation. For perpetual equinox conditions, both climate states are stable below a certain threshold value of background soil albedo (α) while above that only the CW state is stable. Starting from the HD state and increasing α above the threshold causes an abrupt shift from the HD state to the CW state resulting in a sudden cooling of about 35°C globally which is of the order of the temperature difference between the present-day and the Snowball Earth state. In contrast to the Snowball Earth instability, we find that the sudden cooling in our study is driven by the cloud albedo feedback rather than the snow-albedo feedback. Also, when α in the CW state is reduced back to zero the land planet does not display a closed hysteresis. Our study also has implications for the habitability of Earth-like land planets. At the inner edge of the habitable zone, the higher cloud cover in the CW state cools the planet and may prevent the onset of a runaway greenhouse state. At the outer

Surface characteristics at the six sites where spacecraft have successfully landed on Mars can be related favorably to their signatures in remotely sensed data from orbit and from the Earth. Comparisons of the rock abundance, types and coverage of soils (and their physical properties), thermal inertia, albedo, and topographic slope all agree with orbital remote sensing estimates and show that the materials at the landing sites can be used as ground truth for the materials that make up most of the equatorial and mid- to moderately high-latitude regions of Mars. The six landing sites sample two of the three dominant global thermal inertia and albedo units that cover ~80% of the surface of Mars. The Viking, Spirit, Mars Pathfinder, and Phoenix landing sites are representative of the moderate to high thermal inertia and intermediate to high albedo unit that is dominated by crusty, cloddy, blocky or frozen soils (duricrust that may be layered) with various abundances of rocks and bright dust. The Opportunity landing site is representative of the moderate to high thermal inertia and low albedo surface unit that is relatively dust free and composed of dark eolian sand and/or increased abundance of rocks. Rock abundance derived from orbital thermal differencing techniques in the equatorial regions agrees with that determined from rock counts at the surface and varies from ~3-20% at the landing sites. The size-frequency distributions of rocks >1.5 m diameter fully resolvable in HiRISE images of the landing sites follow exponential models developed from lander measurements of smaller rocks and are continuous with these rock distributions indicating both are part of the same population. Interpretation of radar data confirms the presence of load bearing, relatively dense surfaces controlled by the soil type at the landing sites, regional rock populations from diffuse scattering similar to those observed directly at the sites, and root-mean-squared slopes that compare favorably

The elevation of the surface of the ocean and freshwater bodies on land holds key information on many important processes of the Earth System. The elevation of the ocean surface, called ocean surface topography, has been measured by conventional nadirlooking radar altimeter for the past two decades. The data collected have been used for the study of large-scale circulation and sea level change. However, the spatial resolution of the observations has limited the study to scales larger than about 200 km, leaving the smaller scales containing substantial kinetic energy of ocean circulation that is responsible for the flux of heat, dissolved gas and nutrients between the upper and the deep ocean. This flux is important to the understanding of the ocean's role in regulatingfuture climate change.The elevation of the water bodies on land is a key parameter required for the computation of storage and discharge of freshwater in rivers, lakes, and wetlands. Globally, the spatial and temporal variability of water storage and discharge is poorly known due to the lack of well-sampled observations. In situ networks measuring river flows are declining worldwide due to economic and political reasons. Conventional altimeter observations suffers from the complexity of multiple peaks caused by the reflections from water, vegetation canopy and rough topography, resulting in much less valid data over land than over the ocean. Another major limitation is the large inter track distance preventing good coverage of rivers and other water bodies.This document provides descriptions of a new measurement technique using radar interferometry to obtain wide-swath measurement of water elevation at high resolution over both the ocean and land. Making this type of measurement, which addresses the shortcomings of conventional altimetry in both oceanographic and hydrologic applications, is the objective of a mission concept called Surface Water and Ocean Topography (SWOT), which was recommended by

Landsurface albedo is one of the significant geophysical variables affecting the Earth's climate and controlling the surface radiation budget. Topography leads to the formation of shadows and the redistribution of incident radiation, which complicates the modeling and estimation of the landsurface albedo. Some studies show that neglecting the topography effect may lead to significant bias in estimating the landsurface albedo for the sloping terrain. However, for the composite sloping terrain, the topographic effects on the albedo remain unclear. Accurately estimating the sub-topographic effect on the landsurface albedo over the composite sloping terrain presents a challenge for remote sensing modeling and applications. In our study, we focus on the development of a simplified estimation method for landsurface albedo including black-sky albedo (BSA) and white-sky albedo (WSA) of the composite sloping terrain at a kilometer scale based on the fine scale DEM (30m) and quantitatively investigate and understand the topographic effects on the albedo. The albedo is affected by various factors such as solar zenith angle (SZA), solar azimuth angle (SAA), shadows, terrain occlusion, and slope and aspect distribution of the micro-slopes. When SZA is 30°, the absolute and relative deviations between the BSA of flat terrain and that of rugged terrain reaches 0.12 and 50%, respectively. When the mean slope of the terrain is 30.63° and SZA=30°, the absolute deviation of BSA caused by SAA can reach 0.04. The maximal relative and relative deviation between the WSA of flat terrain and that of rugged terrain reaches 0.08 and 50%. These results demonstrate that the topographic effect has to be taken into account in the albedo estimation.

Land-surface influences on weather and climate are reviewed. The interrelationship of vegetation, evapotranspiration, atmospheric circulation, and climate is discussed. Global precipitation, soil moisture, the seasonal water cycle, heat transfer, and atmospheric temperature are among the parameters considered in the context of a general biosphere model.

Landsurface temperature (LST) is a key input for physically-based retrieval algorithms of hydrological states and fluxes. Yet, it remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observation...

A great number of landsurface phenoloy (LSP) data have been produced from various coarse resolution satellite datasets and detection algorithms across regional and global scales. Unlike field- measured phenological events which are quantitatively defined with clear biophysical meaning, current LSP ...

Significant seasonal variations in warming are projected in some regions, especially central Europe, the southeastern U.S., and central South America. Europe in particular may experience up to 2°C more warming during June, July, and August than in the annual mean, enhancing the risk of extreme summertime heat. Previous research has shown that heat waves in Europe and other regions are tied to seasonal soil moisture variations, and that in general land-surface feedbacks have a strong effect on seasonal temperature anomalies. In this study, we show that the seasonal anomalies in warming are also due in part to land-surface feedbacks. We find that in regions with amplified warming during the hot season, surface soil moisture levels generally decline and Bowen ratios increase as a result of a preferential partitioning of incoming energy into sensible vs. latent. The CMIP5 model suite shows significant variability in the strength of land-atmosphere coupling and in projections of future precipitation and soil moisture. Due to the dependence of seasonal warming on land-surface processes, these inter-model variations influence the projected summertime warming amplification and contribute to the uncertainty in projections of future extreme heat.

Climate and land use/cover change are among the most pervasive issues facing the Southeastern United States, including the Savannah River basin in South Carolina and Georgia. Land use directly affects the natural environment across the Savannah River basin and it is important to analyze these impacts. The objectives of this study are to: 1) determine the classes and the distribution of land cover in the Savannah River basin; 2) identify the spatial and the temporal change of the land cover that occurs as a consequence of land use change in the area; and 3) discuss the potential effects of land use change in the Savannah River basin. The land cover maps were produced using random forest supervised classification at four time periods for a total of thirteen common land cover classes with overall accuracy assessments of 79.18% (1999), 79.41% (2005), 76.04% (2009), and 76.11% (2015). The major land use change observed was due to the deforestation and reforestation of forest areas during the entire study period. The change detection results using the normalized difference vegetation index (NDVI) indicated that the proportion areas of the deforestation were 5.93% (1999-2005), 4.63% (2005-2009), and 3.76% (2009-2015), while the proportion areas of the reforestation were 1.57% (1999-2005), 0.44% (2005-2009), and 1.53% (2009-2015). These results not only indicate land use change, but also demonstrate the advantage of utilizing Google Earth Engine and the public archive database in its platform to track and monitor this change over time.

Through the CaPE Hydrometeorology Project, we have developed an understanding of some of the unique data quality issues involved in assimilating data of disparate types for regional-scale hydrologic modeling within a GIS framework. Among others, the issues addressed here include the development of adequate validation of the surface water budget, implementation of the STATSGO soil data set, and implementation of a remote sensing-derived landcover data set to account for surface heterogeneity. A model of landsurface processes has been developed and used in studies of the sensitivity of surface fluxes and runoff to soil and landcover characterization. Results of these experiments have raised many questions about how to treat the scale-dependence of landsurface-atmosphere interactions on spatial and temporal variability. In light of these questions, additional modifications are being considered for the Marshall LandSurface Processes Model. It is anticipated that these techniques can be tested and applied in conjunction with GCIP activities over regional scales.

This review covers a wide range of surface analytical techniques: X-ray photoelectron spectroscopy (XPS), scanning photoelectron microscopy (SPEM), photoemission electron microscopy (PEEM), dynamic and static secondary ion mass spectroscopy (SIMS), electron backscatter diffraction (EBSD), atomic force microscopy (AFM). Others that are relatively less widely used but are also important to the Earth Sciences are also included: Auger electron spectroscopy (AES), low energy electron diffraction (LEED) and scanning tunnelling microscopy (STM). All these techniques probe only the very top sample surface layers (sub-nm to several tens of nm). In addition, we also present several other techniques i.e. Raman microspectroscopy, reflection infrared (IR) microspectroscopy and quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN) that penetrate deeper into the sample, up to several μm, as all of them are fundamental analytical tools for the Earth Sciences. Grazing incidence synchrotron techniques, sensitive to surface measurements, are also briefly introduced at the end of this review. (Scanning) transmission electron microscopy (TEM/STEM) is a special case that can be applied to characterisation of mineralogical and geological sample surfaces. Since TEM/STEM is such an important technique for Earth Scientists, we have also included it to draw attention to the capability of TEM/STEM applied as a surface-equivalent tool. While this review presents most of the important techniques for the Earth Sciences, it is not an all-inclusive bibliography of those analytical techniques. Instead, for each technique that is discussed, we first give a very brief introduction about its principle and background, followed by a short section on approaches to sample preparation that are important for researchers to appreciate prior to the actual sample analysis. We then use examples from publications (and also some of our known unpublished results) within the Earth Sciences

Love and Rayleigh waves propagating on the surface of the Earth exhibit path, phase and amplitude anomalies as a result of the lateral heterogeneity of the mantle. In the JWKB approximation, these anomalies can be determined by tracing surface wave trajectories, and calculating phase and amplitude anomalies along them. A time- or frequency -domain JWKB analysis yields local eigenfunctions, local dispersion relations, and conservation laws for the surface wave energy. The local dispersion relations determine the surface wave trajectories, and the energy equations determine the surface wave amplitudes. On an anisotrophic Earth model the local dispersion relation and the local vertical eigenfunctions depend explicitly on the direction of the local wavevector. Apart from the usual dynamical phase, which is the integral of the local wavevector along a raypath, there is an additional variation is phase. This additional phase, which is an analogue of the Berry phase in adiabatic quantum mechanics, vanishes in a waveguide with a local vertical two-fold symmetry axis or a local horizontal mirror plane. JWKB theory breaks down in the vicinity of caustics, where neighboring rays merge and the surface wave amplitude diverges. Based upon a potential representation of the surface wave field, a uniformly valid Maslov theory can be obtained. Surface wave trajectories are determined by a system of four ordinary differential equations which define a three-dimensional manifold in four-dimensional phase space (theta,phi,k_theta,k _phi), where theta is colatitude, phi is longitude, and k_theta and k _phi are the covariant components of the wavevector. There are no caustics in phase space; it is only when the rays in phase space are projected onto configuration space (theta,phi), the mixed spaces (k_theta,phi ) and (theta,k_phi), or onto momentum space (k_theta,k _phi), that caustics occur. The essential strategy is to employ a mixed or momentum space representation of the wavefield in

Land-level lowering or land subsidence is a consequence of many local- and regional-scale physical, chemical or biologic processes affecting soils and geologic materials. The principal processes can be natural or anthropogenic, and include consolidation or compaction, karst or pseudokarst, hydrocompaction of collapsible soils, mining, oxidation of organic soils, erosive piping, tectonism, and volcanism. In terms of affected area, there are two principal regional-scale anthropogenic processes—compaction of compressible subsurface materials owing to the extraction of subsurface fluids (principally groundwater, oil and gas) and oxidation and compaction accompanying drainage of organic soils—which cause significant hazards related to flooding and infrastructure damage that are amenable to resource management measures. The importance of even small magnitude (< 10 mm/yr) subsidence rates in coastal areas is amplified by its contribution to relative sea-level rise compared to estimated rates of rising eustatic sea levels (2-3 mm/yr) attributed to global climate change. Multi- or interdisciplinary [scientific] studies, including those focused on geodetic, geologic, geophysical, hydrologic, hydrogeologic, geomechanical, geochemical, and biologic factors, improve understanding of these subsidence processes. Examples include geodetic measurement and analysis techniques, such as Global Positioning System (GPS), Light Detection and Ranging (LiDAR) and Interferometric Synthetic Aperture Radar (InSAR), which have advanced our capabilities to detect, measure and monitor land-surface motion at multiple scales. Improved means for simulating aquifer-system and hydrocarbon-reservoir deformation, and the oxidation and compaction of organic soils are leading to refined predictive capabilities. The role of interdisciplinary earth science in improving the characterization of land subsidence attributed to subsurface fluid withdrawals and the oxidation and compaction of organic soils is

The global fields of normal monthly soil moisture and landsurface evapotranspiration are derived with a simple water budget model that has precipitation and potential evapotranspiration as inputs. The precipitation is observed and the potential evapotranspiration is derived from the observed surface air temperature with the empirical regression equation of Thornthwaite (1954). It is shown that at locations where the net surface radiation flux has been measured, the potential evapotranspiration given by the Thornthwaite equation is in good agreement with those obtained with the radiation-based formulations of Priestley and Taylor (1972), Penman (1948), and Budyko (1956-1974), and this provides the justification for the use of the Thornthwaite equation. After deriving the global fields of soil moisture and evapotranspiration, the assumption is made that the potential evapotranspiration given by the Thornthwaite equation and by the Priestley-Taylor equation will everywhere be about the same; the inverse of the Priestley-Taylor equation is used to obtain the normal monthly global fields of net surface radiation flux minus ground heat storage. This and the derived evapotranspiration are then used in the equation for energy conservation at the surface of the earth to obtain the global fields of normal monthly sensible heat flux from the landsurface to the atmosphere.

Land-surface models (LSMs) are of growing importance in the world of climate prediction. They are crucial components of larger Earth system models that are aimed at understanding the effects of landsurface processes on the global carbon cycle. The Joint UK Land Environment Simulator (JULES) is the land-surface model used by the UK Met Office. It has been automatically differentiated using commercial software from FastOpt, resulting in an analytical gradient, or 'adjoint', of the model. Using this adjoint, the adJULES parameter estimation system has been developed, to search for locally optimum parameter sets by calibrating against observations. adJULES presents an opportunity to confront JULES with many different observations, and make improvements to the model parameterisation. In the newest version of adJULES, multiple sites can be used in the calibration, to giving a generic set of parameters that can be generalised over plant functional types. We present an introduction to the adJULES system and its applications to data from a variety of flux tower sites. We show that calculation of the 2nd derivative of JULES allows us to produce posterior probability density functions of the parameters and how knowledge of parameter values is constrained by observations.

The interpretation of landforms and environmental archives on Mars with regards to habitability and preservation of traces of life requires a quantitative understanding of the processes that shaped them. Commonly, qualitative similarities in sedimentary rocks between Earth and Mars are used as an analogue to reconstruct the environments in which they formed on Mars. However, flow hydraulics and sedimentation differ between Earth and Mars, requiring a recalibration of models describing runoff, erosion, transport and deposition. Simulation of these processes on Earth is limited because gravity cannot be changed and the trade-off between adjusting e.g. fluid or particle density generates other mismatches, such as fluid viscosity. Computational Fluid Dynamics offer an alternative, but would also require a certain degree of calibration or testing. Parabolic flights offer a possibility to amend the shortcomings of these approaches. Parabolas with reduced gravity last up to 30 seconds, which allows the simulation of sedimentation processes and the measurement of flow hydraulics. This study summarizes the experience gathered during four campaigns of parabolic flights, aimed at identifying potential and limitations of their use as an Earth analogue for surface processes on Mars.

Landsurface radiation and energy budgets are critical to address a variety of scientific and application issues related to climate trends, weather predictions, hydrologic and biogeophysical modeling, and the monitoring of ecosystem health and agricultural crops. This is an introductory paper to t...

The U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001 is widely used as a baseline for national land cover and impervious conditions. To ensure timely and relevant data, it is important to update this base to a more recent time period. A prototype method was developed to update the land cover and impervious surface by individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season from both 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, impervious surface was estimated for areas of change by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain a variety of metropolitan areas. Results from the five study areas show that the vast majority of impervious surface changes associated with urban developments were accurately captured and updated. The approach optimizes mapping efficiency and can provide users a flexible method to generate updated impervious surface at national and regional scales.

NASA and its partners are contributing important observations and expertise to the ongoing response to the April 25, 2015, magnitude 7.8 Gorkha earthquake in Nepal. The quake was the strongest to occur in that area since the 1934 Nepal-Bihar magnitude 8.0 event and caused significant regional damage and a humanitarian crisis. Scientists with the Advanced Rapid Imaging and Analysis project (ARIA), a collaboration between NASA's Jet Propulsion Laboratory, Pasadena, California, and the California Institute of Technology in Pasadena, analyzed interferometric synthetic aperture radar images from the PALSAR-2 instrument on the ALOS-2 satellite operated by the Japan Aerospace Exploration Agency (JAXA) to calculate a map of the deformation of Earth's surface caused by the quake. This false-color map shows the amount of permanent surface movement caused almost entirely by the earthquake, as viewed by the satellite, during a 70-day interval between two ALOS-2 images, acquired February 21 and May 2, 2015. In the map, surface displacements are seen as color contours (or "fringes"), where each color cycle represents 4.7 inches (11.9 centimeters) of surface motion. The contours show the land around Kathmandu has moved toward the satellite by up to 4.6 feet (1.4 meter), or 5.2 feet (1.6 meters) if we assume purely vertical motion. Areas without the color contours have snow or heavy vegetation that affects the radar measurements. Scientists use these maps to build detailed models of the fault and associated land movements to better understand the impact on future earthquake activity. The PALSAR-2 data were provided by JAXA through the Committee on Earth Observation Satellite (CEOS) in support of the response effort. The background image is from Google Earth. http://photojournal.jpl.nasa.gov/catalog/PIA19383

The research carried out in the Earth Sciences in NASA and at NASA's Goddard Space Flight Center will be the focus of the presentations. In addition, one research project that links sea surface temperature to epidemics in Africa will be highlighted. At GSFC research interests span the full breath of disciplines in Earth Science. Branches and research groups focus on areas as diverse as planetary geomagnetics and atmospheric chemistry. These organizations focus on atmospheric sciences (atmospheric chemistry, climate and radiation, regional processes, atmospheric modeling), hydrological sciences (snow, ice, oceans, and seasonal-to-interannual prediction), terrestrial physics (geology, terrestrial biology, land-atmosphere interactions, geophysics), climate modeling (global warming, greenhouse gases, climate change), on sensor development especially using lidar and microwave technologies, and on information technologies, that enable support of scientific and technical research.

Human societies have emerged as a global force capable of transforming the biosphere, hydrosphere, lithosphere, atmosphere and climate. As a result, the long-term dynamics of the Earth system can no longer be understood or predicted without understanding their coupling with human societal dynamics. Here, a general causal theory is presented to explain why behaviorally modern humans, unlike any prior multicellular species, gained this unprecedented capacity to reshape the Earth system and how this societal capacity has changed from the Pleistocene to the present and future. Sociocultural niche construction theory, building on existing theories of ecosystem engineering, niche construction, the extended evolutionary synthesis, cultural evolution, ultrasociality and social change, can explain both the long-term upscaling of human societies and their unprecedented capacity to transform the Earth system. Regime shifts in human sociocultural niche construction, from the clearing of land using fire, to shifting cultivation, to intensive agriculture, to global food systems dependent on fossil fuel combustion, have enabled human societies to scale up while gaining the capacity to reshape the global patterns and processes of biogeography, ecosystems, landscapes, biomes, the biosphere, and ultimately the functioning of the Earth system. Just as Earth's geophysical climate system shapes the long-term dynamics of energy and material flow across the "spheres" of the Earth system, human societies, interacting at global scale to form "human systems", are increasingly shaping the global dynamics of energy, material, biotic and information flow across the spheres of the Earth system, including a newly emerged anthroposphere comprised of human societies and their material cultures. Human systems and the anthroposphere are strongly coupled with climate and other Earth systems and are dynamic in response to evolutionary changes in human social organization, cooperative ecosystem

Release of NLCD 2006 provides the first wall-to-wall land-cover change database for the conterminous United States from Landsat Thematic Mapper (TM) data. Accuracy assessment of NLCD 2006 focused on four primary products: 2001 land cover, 2006 land cover, land-cover change between 2001 and 2006, and impervious surface change between 2001 and 2006. The accuracy assessment was conducted by selecting a stratified random sample of pixels with the reference classification interpreted from multi-temporal high resolution digital imagery. The NLCD Level II (16 classes) overall accuracies for the 2001 and 2006 land cover were 79% and 78%, respectively, with Level II user's accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates. Level I (8 classes) accuracies were 85% for NLCD 2001 and 84% for NLCD 2006. The high overall and user's accuracies for the individual dates translated into high user's accuracies for the 2001–2006 change reporting themes water gain and loss, forest loss, urban gain, and the no-change reporting themes for water, urban, forest, and agriculture. The main factor limiting higher accuracies for the change reporting themes appeared to be difficulty in distinguishing the context of grass. We discuss the need for more research on land-cover change accuracy assessment.

The spontaneous organization of convection into clusters, or self-aggregation, demonstrably changes the nature and statistics of precipitation. While there has been much recent progress in this area, the processes that control self-aggregation are still poorly understood. Most of the work to date has focused on self-aggregation over ocean-like surfaces, but it is particularly pressing to understand what controls convective aggregation over land, since the associated change in precipitation statistics—between non-aggregated and aggregated convection—could have huge impacts on society and infrastructure. Radiative-convective equilibrium (RCE), has been extensively used as an idealized framework to study the tropical atmosphere. Self-aggregation manifests in numerous numerical models of RCE, nevertheless, there is still a lack of understanding in how it relates to convective organization in the observed world. Numerous studies have examined self-aggregation using idealized Cloud Resolving Models (CRMs) and General Circulation Models over the ocean, however very little work has been done on RCE and self-aggregation over land. Idealized models of RCE over ocean have shown that aggregation is sensitive to sea surface temperature (SST), more intense precipitation occurs in aggregated systems, and a variety of feedbacks—such as surface flux, cloud radiative, and upgradient moisture transport— contribute to the maintenance of aggregation, however it is not clear if these results apply over land. Progress in this area could help relate understanding of self-aggregation in idealized simulations to observations. In order to explore the behavior of self-aggregation over land, we use a CRM to simulate idealized RCE over land. In particular, we examine the aggregation of convection and how it compares with aggregation over ocean. Based on previous studies, where a variety of different CRMs exhibit a SST threshold below which self-aggregation does not occur, we hypothesize

Understanding the response of the Earth's climate system to anthropogenic perturbation has been a pressing priority for society since the late 1980s. However, recent years have seen a major paradigm shift in how such an understanding can be reached. Climate change demands analysis within an integrated 'Earth-system' framework, taken to encompass the suite of interacting physical, chemical, biological and human processes that, in transporting and transforming materials and energy, jointly determine the conditions for life on the whole planet. This is a highly complex system, characterized by multiple nonlinear responses and thresholds, with linkages often between apparently disparate components. The interconnected nature of the Earth system is wonderfully illustrated by the diverse roles played by atmospheric transport of mineral 'dust', particularly in its capacity as a key pathway for the delivery of nutrients essential to plant growth, not only on land, but perhaps more importantly, in the ocean. Dust therefore biogeochemically links land, air and sea. This paper reviews the biogeochemical role of mineral dust in the Earth system and its interaction with climate, and, in particular, the potential importance of both past and possible future changes in aeolian delivery of the micro-nutrient iron to the ocean. For instance, if, in the future, there was to be a widespread stabilization of soils for the purpose of carbon sequestration on land, a reduction in aeolian iron supply to the open ocean would occur. The resultant weakening of the oceanic carbon sink could potentially offset much of the carbon sequestered on land. In contrast, during glacial times, enhanced dust supply to the ocean could have 'fertilized' the biota and driven atmospheric CO(2) lower. Dust might even play an active role in driving climatic change; since changes in dust supply may affect climate, and changes in climate, in turn, influence dust, a 'feedback loop' is formed. Possible feedback

The state of Delaware has seen significant increases in population along the coastline in the past three decades. With this increase in population have come changes to the landsurface, as forest and farmland has been converted to residential and commercial purposes, causing changes in the surface roughness, temperature, and land-atmosphere fluxes. There is also a semi-permanent upwelling center in the spring and summer outside the Delaware Bay mouth that significantly changes the structure of the sea surface temperature both inside and outside the Bay. Through a series of high resolution modeling and observational studies, we have determined that in cases of strong synoptic forcing, the impact of the land-surface on the boundary layer properties can be advected offshore, creating a false coastline and modifying the location and timing of the sea breeze circulation. In cases of weak synoptic forcing, the influence of the upwelling and the tidal circulation of the Delaware Bay waters can greatly change the location, strength, and penetration of the sea breeze. Understanding the importance of local variability in the surface-atmosphere interactions on the sea breeze can lead to improved prediction of sea breeze onset, penetration, and duration which is important for monitoring air quality and developing offshore wind power production.

The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many meteorological processes. High-resolution, accurate representations of surface properties such as sea-surface temperature (SST), soil temperature and moisture content, ground fluxes, and vegetation are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of sensible weather. The NASA Short-term Prediction Research and Transition (SPoRT) Center has been conducting separate studies to examine the impacts of high-resolution land-surface initialization data from the Goddard Space Flight Center Land Information System (LIS) on subsequent WRF forecasts, as well as the influence of initializing WRF with SST composites derived from the MODIS instrument. This current project addresses the combined impacts of using high-resolution lower boundary data over both land (LIS data) and water (MODIS SSTs) on the subsequent daily WRF forecasts over Florida during May 2004. For this experiment, the WRF model is configured to run on a nested domain with 9- km and 3-kin grid spacing, centered on the Florida peninsula and adjacent coastal waters of the Gulf of Mexico and Atlantic Ocean. A control configuration of WRF is established to take all initial condition data from the NCEP Eta model. Meanwhile, two WRF experimental runs are configured to use high-resolution initialization data from (1) LIS land-surface data only, and (2) a combination of LIS data and high-resolution MODIS SST composites. The experiment involves running 24-hour simulations of the control WRF configuration, the MS-initialized WRF, and the LIS+MODIS-initialized WRF daily for the entire month of May 2004. All atmospheric data for initial and boundary conditions for the Control, LIS, and LIS+MODIS runs come from the NCEP Eta model on a 40-km grid. Verification statistics are generated at landsurface observation sites and buoys, and the impacts

Accelerating urbanization affects regional climate as the result of changing land cover and land use (LCLU). Urban land cover composition may provide valuable insight into relationships among urbanization, air, and land-surface temperature (Ta and LST, respectively). Climate may alter these relationships, where hotter climates experience larger LULC effects. To address these hypotheses we examined links between Ta, LST, LCLU, and vegetation across an urban coastal to desert climate gradient in southern California, USA. Using surface temperature radiometers, continuously measuring LST on standardized asphalt, concrete, and turf grass surfaces across the climate gradient, we found a 7.2°C and 4.6°C temperature decrease from asphalt to vegetated cover in the coast and desert, respectively. There is 131% more temporal variation in asphalt than turf grass surfaces, but 37% less temporal variation in concrete than turf grass. For concrete and turf grass surfaces, temporal variation in temperature increased from coast to desert. Using ground-based thermal imagery, measuring LST for 24 h sequences over citrus orchard and industrial use locations, we found a 14.5°C temperature decrease from industrial to orchard land use types (38.4°C and 23.9°C, respectively). Additionally, industrial land use types have 209% more spatial variation than orchard (CV=0.20 and 0.09, respectively). Using a network of 300 Ta (iButton) sensors mounted in city street trees throughout the region and hyperspectral imagery data we found urban vegetation greenness, measured using the normalized difference vegetation index (NDVI), was negatively correlated to Ta at night across the climate gradient. Contrasting previous findings, the closest coupling between NDVI and Ta is at the coast from 0000 h to 0800 h (highest r2 = 0.6, P < 0.05) while relationships at the desert are weaker (highest r2 = 0.38, P < 0.05). These findings indicate that vegetation cover in urbanized regions of southern

Rwanda, a small country with the highest population density in Sub-Saharan Africa, is one of the world's poorest countries. Although agriculture is the backbone of Rwandan economy, agricultural productivity is extremely low. Over 90 % of the population is engaged in subsistence farming and only 52 % of the total landsurface area is arable. Of this land, approximately 165,000 hectares are marshlands, of which only 57 % has been cultivated. Rwandan government has invested in the advancement of agriculture with activities such as irrigation, marshland reclamation, and crop regionalization. In 2001, Ministry of Agriculture and Animal Resources (MINAGRI) released the Rural Sector Support Program (RSSP), which aimed at converting marshlands into rice fields at various development sites across the country. The focus of this project was to monitor rice fields in Rwanda utilizing NASA Earth observations such as Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager. Modified Normalized Difference Water Index (MNDWI) was used to depict the progress of marshland to rice field conversion as it highlights the presence of irrigated rice fields from the surrounding area. Additionally, Decision Support System for Agrotechnology Transfer (DSSAT) was used to estimate rice yield at RSSP sites. Various simulations were run to find perfect conditions for cultivating the highest yield for a given farm. Furthermore, soil erosion susceptibility masks were created by combining factors derived from ASTER, MERRA, and ground truth data using Revised Universal Soil Loss Equation (RUSLE). The end results, maps, and tutorials were delivered to the partners and policy makers in Rwanda to help make informed decisions. It can be clearly seen that Earth observations can be successfully used to monitor agricultural and land management practices as a cost effective method that will enable farmers to improve crop yield production and food security.

Thanks to its large spatio-temporal coverage, the new ESA CCI multi-instruments dataset offers a good opportunity to assess and improve landsurface models parametrization. In this study, the ESA CCI surface soil moisture (SSM) combined product (v2.2) has been compared to the simulated top first layers of the ORCHIDEE LSM (the continental part of the IPSL earth system model), in order to evaluate its potential of improvements with data assimilation techniques. The ambition of the work was to develop a comprehensive comparison methodology by analyzing simultaneously the temporal and spatial structures of both datasets. We analyzed the SSM synoptic, seasonal, and inter-annual variations by decomposing the signals into fast and slow components. ORCHIDEE was shown to adequately reproduce the observed SSM dynamics in terms of temporal correlation. However, these correlation scores are supposed to be strongly influenced by SSM seasonal variability and the quality of the model input forcing. Autocorrelation and spectral analyses brought out disagreements in the temporal inertia of the upper soil moisture reservoirs. By linking our results to land cover maps, we found that ORCHIDEE is more dependent on rainfall events compared to the observations in regions with sparse vegetation cover. These diflerences might be due to a wrong partition of rainfall between soil evaporation, transpiration, runofl and drainage in ORCHIDEE. To refine this analysis, a single value decomposition (SVD) of the co-variability between rainfall provided by WFDEI and soil moisture was pursued over Central Europe and South Africa. It showed that spatio-temporal co-varying patterns between ORCHIDEE and rainfall and the ESA-CCI product and rainfall are in relatively good agreement. However, the leading SVD pattern, which exhibits a strong annual cycle and explains the same portion of covariance for both datasets, explains a much larger fraction of variance for ORCHIDEE than for the ESA-CCI product

Results are reported of a detailed examination of data available for the Apollo lunar landing sites, including the Apollo orbital measurements of six major elements derived from XRF and gamma-ray instruments and geochemical parameters derived from earth-based spectral reflectivity data. Wherever orbital coverage for Apollo landing sites exist, the remote data were correlated with geochemical data derived from the soil sample averages for major geological units and the major rock components associated with these units. Discrepancies were observed between the remote and the soil-anlysis elemental concentration data, which were apparently due to the differences in the extent of exposure of geological units, and, hence, major rock eomponents, in the area sampled. Differences were observed in signal depths between various orbital experiments, which may provide a mechanism for explaining differences between the XRF and other landing-site data.

The Sea LandSurface Temperature Radiometer is a dual view Earth observing instrument developed as part of the European Global Monitoring for Environment and Security programme. It is scheduled for launch on two satellites, Sentinel 3A and 3B in 2014. The instrument detectors are cooled to below 85 K by two split Stirling Cryocoolers running in hot redundancy. These coolers form part of a cryocooler system that includes a support structure and drive electronics. Aspects of the system design, including control and reduction of exported vibration are discussed; and results, including thermal performance and exported vibration from the Engineering Model Cryooler System test campaign are presented.

As part of an effort to map terrestrial ecosystems, the U.S. Geological Survey has generated landsurface form classes to be used in creating maps depicting standardized, terrestrial ecosystem models for the conterminous United States, using an ecosystems classification developed by NatureServe . A biophysical stratification approach, developed for South America and now being implemented globally, was used to model the ecosystem distributions. Since landsurface forms strongly influence the differentiation and distribution of terrestrial ecosystems, they are one of the key input layers in this biophysical stratification. After extensive investigation into various landsurface form mapping methodologies, the decision was made to use the methodology developed by the Missouri Resource Assessment Partnership (MoRAP). MoRAP made modifications to Hammond's landsurface form classification, which allowed the use of 30-meter source data and a 1-km2 window for analyzing the data cell and its surrounding cells (neighborhood analysis). While Hammond's methodology was based on three topographic variables, slope, local relief, and profile type, MoRAP's methodology uses only slope and local relief. Using the MoRAP method, slope is classified as gently sloping when more than 50 percent of the area in a 1-km2 neighborhood has slope less than 8 percent, otherwise the area is considered moderately sloping. Local relief, which is the difference between the maximum and minimum elevation in a neighborhood, is classified into five groups: 0-15 m, 16-30 m, 31-90 m, 91-150 m, and >150 m. The landsurface form classes are derived by combining slope and local relief to create eight landform classes: flat plains (gently sloping and local relief = 90 m), low hills (not gently sloping and local relief = 150 m). However, in the USGS application of the MoRAP methodology, an additional local relief group was used (> 400 m) to capture additional local topographic variation. As a result, low

LandSurface Temperature and Emissivity (LST&E) data are critical variables for studying a variety of Earthsurface processes and surface-atmosphere interactions such as evapotranspiration, surface energy balance and water vapor retrievals. LST&E have been identified as an important Earth System Data Record (ESDR) by NASA and many other international organizations Accurate knowledge of the LST&E is a key requirement for many energy balance models to estimate important surface biophysical variables such as evapotranspiration and plant-available soil moisture. LST&E products are currently generated from sensors in low earth orbit (LEO) such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites as well as from sensors in geostationary Earth orbit (GEO) such as the Geostationary Operational Environmental Satellites (GOES) and airborne sensors such as the Hyperspectral Thermal Emission Spectrometer (HyTES). LST&E products are generated with varying accuracies depending on the input data, including ancillary data such as atmospheric water vapor, as well as algorithmic approaches. NASA has identified the need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. We will discuss the different approaches that can be used to retrieve surface temperature and emissivity from multispectral and hyperspectral thermal infrared sensors using examples from a variety of different sensors such as those mentioned, and planned new sensors like the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Hyperspectral Infrared Imager (HyspIRI). We will also discuss a project underway at NASA to develop a single unified product from some the individual sensor products and assess the errors associated with the product.

In the archetypal "tragedy of the commons" narrative, local farmers pasture their cows on the town common. Soon the common becomes crowded with cows, who graze it bare, and the arrangement of open access to a shared resource ultimately fails. The "tragedy" involves social and physical processes, but the denouement depends on who is telling the story. An economist might argue that the system collapses because each farmer always has a rational incentive to graze one more cow. An ecologist might remark that the rate of grass growth is an inherent control on the common's carrying capacity. And a geomorphologist might point out that processes of soil degradation almost always outstrip processes of soil production. Interdisciplinary research into human-environmental systems still tends to favor disciplinary vantages. In the context of Anthropocene grand challenges - including fundamental insight into dynamics of landscape resilience, and what the dominance of human activities means for processes of change and evolution on the Earth's surface - two disciplines in particular have more to talk about than they might think. Here, I use three examples - (1) beach nourishment, (2) upstream/downstream fluvial asymmetry, and (3) current and historical "land grabbing" - to illustrate a range of interconnections between physical Earth-surface science and common-pool resource economics. In many systems, decision-making and social complexity exert stronger controls on landscape expression than do physical geomorphological processes. Conversely, human-environmental research keeps encountering multi-scale, emergent problems of resource use made 'common-pool' by water, nutrient and sediment transport dynamics. Just as Earth-surface research can benefit from decades of work on common-pool resource systems, quantitative Earth-surface science can make essential contributions to efforts addressing complex problems in environmental sustainability.

Accurate representation of vegetation processes within landsurface models is key to reproducing surface carbon, water and energy fluxes. Photosynthesis determines the amount of CO2 fixated by plants as well as the water lost in transpiration through the stomata. Photosynthesis is calculated in landsurface models using empirical equations based on plant physiological research. It is assumed that CO2 assimilation is either CO2 -limited, radiation -limited ; and in some models export-limited (the speed at which the products of photosynthesis are used by the plant) . Increased levels of atmospheric CO2 concentration tend to enhance photosynthetic activity, but the effectiveness of this fertilization effect is regulated by environmental conditions and the limiting factor in the photosynthesis reaction. The photosynthesis schemes at the 'leaf level' used by landsurface models JULES and CTESSEL have been evaluated against field photosynthesis observations. Also, the response of photosynthesis to radiation, atmospheric CO2 and temperature has been analysed for each model, as this is key to understanding the vegetation response that climate models using these schemes are able to reproduce. Particular emphasis is put on the limiting factor as conditions vary. It is found that while at present day CO2 concentrations export-limitation is only relevant at low temperatures, as CO2 levels rise it becomes an increasingly important restriction on photosynthesis.

The objective of the study was to determine the effect of landingsurface on plantar kinetics during a half-squat landing. Twenty male elite paratroopers with formal parachute landing training and over 2 years of parachute jumping experience were recruited. The subjects wore parachuting boots in which pressure sensing insoles were placed. Each subject was instructed to jump off a platform with a height of 60 cm, and land on either a hard or soft surface in a half-squat posture. Outcome measures were maximal plantar pressure, time to maximal plantar pressure (T-MPP), and pressure-time integral (PTI) upon landing on 10 plantar regions. Compared to a soft surface, hard surface produced higher maximal plantar pressure in the 1st to 4th metatarsal and mid-foot regions, but lower maximal plantar pressure in the 5th metatarsal region. Shorter T- MPP was found during hard surfacelanding in the 1st and 2nd metatarsal and medial rear foot. Landing on a hard surfacelanding resulted in a lower PTI than a soft surface in the 1stphalangeal region. For Chinese paratroopers, specific foot prosthesis should be designed to protect the1st to 4thmetatarsal region for hard surfacelanding, and the 1stphalangeal and 5thmetatarsal region for soft surfacelanding. Key Points Understanding plantar kinetics during the half-squat landing used by Chinese paratroopers can assist in the design of protective footwear. Compared to landing on a soft surface, a hard surface produced higher maximal plantar pressure in the 1st to 4th metatarsal and mid-foot regions, but lower maximal plantar pressure in the 5th metatarsal region. A shorter time to maximal plantar pressure was found during a hard surfacelanding in the 1st and 2nd metatarsals and medial rear foot. Landing on a hard surface resulted in a lower pressure-time integral than landing on a soft surface in the 1st phalangeal region. For Chinese paratroopers, specific foot prosthesis should be designed to protect the 1st to 4th metatarsal

Morphological evidence for ancient channelized flows (fluvial and fluvial-like landforms) exists on the surfaces of all of the inner planets and on some of the satellites of the Solar System. In some cases, the relevant fluid flows are related to a planetary evolution that involves the global cycling of a volatile component (water for Earth and Mars; methane for Saturn's moon Titan). In other cases, as on Mercury, Venus, Earth's moon, and Jupiter's moon Io, the flows were of highly fluid lava. The discovery, in 1972, of what are now known to be fluvial channels and valleys on Mars sparked a major controversy over the role of water in shaping the surface of that planet. The recognition of the fluvial character of these features has opened unresolved fundamental questions about the geological history of water on Mars, including the presence of an ancient ocean and the operation of a hydrological cycle during the earliest phases of planetary history. Other fundamental questions posed by fluvial and fluvial-like features on planetary bodies include the possible erosive action of large-scale outpourings of very fluid lavas, such as those that may have produced the remarkable canali forms on Venus; the ability of exotic fluids, such as methane, to create fluvial-like landforms, as observed on Saturn's moon, Titan; and the nature of sedimentation and erosion under different conditions of planetary surface gravity. Planetary fluvial geomorphology also illustrates fundamental epistemological and methodological issues, including the role of analogy in geomorphological/geological inquiry.

Our Earth's environment is experiencing rapid changes due to natural variability and human activities. To monitor, understand and predict environment changes to meet the economic, social and environmental needs, use of long-term high-quality satellite data products is critical. The Global LAndSurface Satellite (GLASS) product suite, generated at Beijing Normal University, currently includes 12 products, including leaf area index (LAI), broadband shortwave albedo, broadband longwave emissivity, downwelling shortwave radiation and photosynthetically active radiation, landsurface skin temperature, longwave net radiation, daytime all-wave net radiation, fraction of absorbed photosynetically active radiation absorbed by green vegetation (FAPAR), fraction of green vegetation coverage, gross primary productivity (GPP), and evapotranspiration (ET). Most products span from 1981-2014. The algorithms for producing these products have been published in the top remote sensing related journals and books. More and more applications have being reported in the scientific literature. The GLASS products are freely available at the Center for Global Change Data Processing and Analysis of Beijing Normal University (http://www.bnu-datacenter.com/), and the University of Maryland Global Land Cover Facility (http://glcf.umd.edu). After briefly introducing the basic characteristics of GLASS products, we will present some applications on the long-term environmental changes detected from GLASS products at both global and local scales. Detailed analysis of regional hotspots, such as Greenland, Tibetan plateau, and northern China, will be emphasized, where environmental changes have been mainly associated with climate warming, drought, land-atmosphere interactions, and human activities.

The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the de facto standard for precipitation information across Earthssurface for hydrometeorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth owing to temporal sampling resolutions, periods of operation, data latency, and data access. Numbers of gauges range from a few thousand available in nearreal time to about 100,000 for all official gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 and 3,000 m2. For comparison, the center circle of a soccer pitch or tennis court is about 260 m2. Although each gauge should represent more than just the gauge orifice, autocorrelation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC)available gauge is independent and represents a surrounding area of 5-km radius, this represents only about 1 of Earthssurface. The situation is further confounded for snowfall, which has a greater measurement uncertainty.

The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the de facto standard for precipitation information across Earthssurface for hydrometeorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth owing to temporal sampling resolutions, periods of operation, data latency, and data access. Numbers of gauges range from a few thousand available in near real time to about 100,000 for all official gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 and 3,000 sq m. For comparison, the center circle of a soccer pitch or tennis court is about 260 sq m. Although each gauge should represent more than just the gauge orifice, autocorrelation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC) available gauge is independent and represents a surrounding area of 5-km radius, this represents only about 1% of Earthssurface. The situation is further confounded for snowfall, which has a greater measurement uncertainty.

Currently, the land-surface region sequesters 25% of global CO2 emissions. In addition to climate change, increasing atmospheric CO2 concentrations, fertilisation and nitrogen deposition, this sink is thought to be largely due to land management. When applied deliberately to enhance the terrestrial carbon sink strength, this land management may have unintended effects on the energy budget, potentially offsetting the radiative effect of carbon sequestration. As with other landsurface models, the present release of ORCHIDEE (the landsurface model of the IPSL Earth system model) has difficulties in reproducing consistently observed energy balances (Pitman et al., 2009; Jimenez et al., 2011; de Noblet-Ducoudré et al., 2011). Hence, the model must be improved to be better able to study the radiative effect of forest management and land use change. This observation serves as a starting point in this research - improving the level of detail in energy balance simulations of the surface layer. We here outline the structure of a new detailed and practical simulation of the energy budget that is currently under development within the surface model ORCHIDEE, and will be coupled to the atmospheric model LMDZ. The most detailed simulations of the surface layer energy budget are detailed iterative multi-layer canopy models, such as Ogeé et al. (2003), which are linked to specific measurement sites and do not interact with the atmosphere. In this current project, we aim to create a model that will implement the insights obtained in those previous studies and improve upon the present ORCHIDEE parameterisation, but will run stably and efficiently when coupled to an atmospheric model. This work involves a replacement of the existing allocation of 14 different types of vegetation within each surface tile (the 'Plant Functional Types') by a more granular scheme that can be modified to reflect changes in attributes such as vegetation density, leaf type, distribution (clumping

In southeastern Jackson County and northwestern Matagorda County, the landsurface subsided more than 1.5 feet (0.46 meter) during 1943-73 as a result of ground-water withdrawals. Withdrawals of oil, gas, and associated ground water caused more than 5 feet (1.5 meters) of subsidence during 1942-75 in the western part of Corpus Christi in Nueces County.

This paper presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) LandSurface Temperature (LST) product provided by the National Aeronautics and Space Administration (NASA) Terra and Aqua Earth Observing System satellites using in situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center - Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January-April) and 2014 (February-April). A total of 314 day and night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. Additionally, this investigation incorporated supplementary analyses using meteorological CREST-SAFE in situ variables (i.e. wind speed, cloud cover, incoming solar radiation) to study their effects on in situ snow surface temperature (T-skin) and T-air. Furthermore, a single pixel (1km2) and several spatially averaged pixels were used for satellite LST validation by increasing the MODIS window size to 5x5, 9x9, and 25x25 windows for comparison. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and nighttime values. Results indicate that, although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C), both suggesting that MODIS LST retrievals are reliable for similar land cover classes and atmospheric conditions. Results from the CREST-SAFE in situ variables' analyses indicate that T-air is commonly higher than T-skin, and that a lack of cloud cover results in: lower T-skin and higher T-air minus T-skin difference (T-diff). Additionally, the study revealed that T-diff is inversely proportional to cloud cover, wind speed, and incoming solar radiation. Increasing the MODIS window size

The U.S. Northeast includes some of the nation's most populated cities and their supporting hinterlands, with an urban corridor spanning from Maine to Virginia. The megaregion's centuries-long history of landscape transformations has had enduring impact on the region's hydrology, ecosystems and socioeconomy. Driven by policy decisions made in the next decade, future landscape changes will also interplay with climate change, with multi-decadal effects that are currently poorly understood. While existing national and global land cover trajectories will play an important role in understanding these future impacts, they do not allow for investigation of many issues of interest to regional stakeholders, such as local zoning and suburban sprawl, the development of a regional food system, or varying rates of natural lands protection. Existing land cover trajectories also do not usually provide the detail needed as input drivers for earth system models, such as disaggregated vegetation types or harmonized time series of infrastructure management. We discuss the development of a simple land use/land cover allocation scheme to develop such needed trajectories, their implementation for 4 regional socioeconomic pathways developed collaboratively with regional stakeholders, and their preliminary use in regional ecosystem modeling.

Compared with other European countries The Netherlands has a relatively high level of pesticide consumption, particularly in agriculture. Many of the compounds concerned end up in surface waters. Surface water quality is routinely monitored and numerous pesticides are found to be present in high concentrations, with various standards being regularly exceeded. Many standards-breaching pesticides exhibit regional patterns that can be traced back to land use. These patterns have been statistically analysed by correlating surface area per land use category with standards exceedance per pesticide, thereby identifying numerous significant correlations with respect to breaches of both the ecotoxicological standard (Maximum Tolerable Risk, MTR) and the drinking water standard. In the case of the MTR, greenhouse horticulture, floriculture and bulb-growing have the highest number as well as percentage of standard-breaching pesticides, despite these market segments being relatively small in terms of area cropped. Cereals, onions, vegetables, perennial border plants and pulses are also associated with many pesticides that exceed the drinking water standard. When a correction is made for cropped acreage, cereals and potatoes also prove to be a major contributor to monitoring sites where the MTR standard is exceeded. Over the period 1998-2006 the land-use categories with the most and highest percentage of standards-exceeding pesticides (greenhouse horticulture, bulb-growing and flower cultivation) showed an increase in the percentage of standards-exceeding compounds.

Uncertainties in the retrievals of microwave landsurface emissivities were quantified over two types of landsurfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two landsurface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 14% (312 K) over desert and 17% (320 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.52% (26 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 1017 K under the most severe conditions.

Previous research has shown that landsurface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about landsurface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting landsurface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

Using SPOT/VGT NDVI time series images (2002-2009) and MODIS/LST images (2002-2009) smoothed by a Savitzky-Golay filter, the landsurface phenology (LSP) and landsurface temperature (LST), respectively, are extracted for six cities in the Yangtze River Delta, China, including Shanghai, Hangzhou, Nanjing, Changzhou, Wuxi, and Suzhou. The trends of the averaged LSP and LST are analyzed, and the relationship between these values is revealed along the urban-rural gradient. The results show that urbanization advances the start of the growing season, postpones the end of the growing season, prolongs the growing season length (GSL), and reduces the difference between maximal NDVI and minimal NDVI in a year (NDVIamp). More obvious changes occur in surface vegetation phenology as the urbanized area is approached. The LST drops monotonously and logarithmically along the urban-rural gradient. Urbanization generally affects the LSP of the surrounding vegetation within 6 km to the urban edge. Except for GSL, the difference in the LSP between urban and rural areas has a significant logarithmic relationship with the distance to the urban edge. In addition, there is a very strong linear relationship between the LSP and the LST along the urban-rural gradient, especially within 6 km to the urban edge. The correlations between LSP and gross domestic product and population density reveal that human activities have considerable influence on the landsurface vegetation growth.

Plants influence climate through changes in the landsurface biophysics (albedo, transpiration) and concentrations of the atmospheric greenhouse gases. One of the interesting periods to investigate a climatic role of terrestrial biosphere is the Holocene, when, despite of the relatively steady global climate, the atmospheric CO2 grew by about 20 ppm from 7 kyr BP to pre-industrial. We use a new setup of the Max Planck Institute Earth System Model MPI-ESM1 consisting of the latest version of the atmospheric model ECHAM6, including the landsurface model JSBACH3 with carbon cycle and vegetation dynamics, coupled to the ocean circulation model MPI-OM, which includes the HAMOCC model of ocean biogeochemistry. The model has been run for several simulations over the Holocene period of the last 8000 years under the forcing data sets of orbital insolation, atmospheric greenhouse gases, volcanic aerosols, solar irradiance and stratospheric ozone, as well as land-use changes. In response to this forcing, the land carbon storage increased by about 60 PgC between 8 and 4 kyr BP, stayed relatively constant until 2 kyr BP, and decreased by about 90 PgC by 1850 AD due to land use changes. Vegetation and soil carbon changes significantly affected atmospheric CO2 during the periods of strong volcanic eruptions. In response to the eruption-caused cooling, the land initially stores more carbon as respiration decreases, but then it releases even more carbon due to productivity decrease. This decadal- scale variability helps to quantify the vegetation and land carbon feedbacks during the past periods when the temporal resolution of the ice-core CO2 record is not sufficient to capture fast CO2 variations. From a set of Holocene simulations with prescribed or interactive atmospheric CO2, we get estimates of climate-carbon feedback useful for future climate studies. Members of the Hamburg Holocene Team: Jürgen Bader1, Sebastian Bathiany2, Victor Brovkin1, Martin Claussen1,3, Traute Cr

Spectral landsurface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. The availability of global albedo data over a large range of spectral channels and at high spatial resolution has dramatically improved with the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard NASA s Earth Observing System (EOS) Terra spacecraft in December 1999. However, lack of spatial and temporal coverage due to cloud and snow effects can preclude utilization of official products in production and research studies. We report on a technique used to fill incomplete MOD43 albedo data sets with the intention of providing complete value-added maps. The technique is influenced by the phenological concept that within a certain area, a pixel s ecosystem class should exhibit similar growth cycle events over the same time period. The shape of an area s phenological temporal curve can be imposed upon existing pixel-level data to fill missing temporal points. The methodology will be reviewed by showcasing 2001 global and regional results of complete albedo and NDVl data sets.

Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 200-02010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approach An independent validation dataset was also developed using Google Earth imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 +/- 7.84 sq km.

Rare earth elements (REE) play a critical role in many emerging clean energy technologies, including high-power magnets, wind turbines, solar panels, hybrid/electric vehicle batteries and lamp phosphors. In order to sustain demand for such technologies given current domestic REE shortages, there is a need to develop new approaches for ore processing/refining and recycling of REE-containing materials. To this end, we have developed a microbially-mediated bioadsorption strategy with application towards enrichment of REE from complex mixtures. Specifically, the bacterium Caulobacter crescentus was genetically engineered to display lanthanide binding tags (LBTs), short peptides that possess high affinity and specificity for rare earth elements, on its cell surface S-layer protein. Under optimal conditions, LBT-displayed cells adsorbed greater than 5-fold more REE than control cells lacking LBTs. Competition binding experiments with a selection of REEs demonstrated that our engineered cells could facilitate separation of light- from heavy- REE. Importantly, binding of REE onto our engineered strains was much more favorable compared to non-REE metals. Finally, REE bound to the cell surface could be stripped off using citrate, providing an effective and non-toxic REE recovery method. Together, this data highlights the potential of our approach for selective REE enrichment from REE containing mixtures.

EarthShape - "Earthsurface shaping by biota" is a 6-year priority research program funded by the German science foundation (DFG-SPP 1803) that performs soil- and landscape-scale critical zone research at 4 locations along a climate gradient in Chile, South America. The program is in its first year and involves an interdisciplinary collaboration between geologists, geomorphologists, ecologists, soil scientists, microbiologists, geophysicists, geochemists, hydrogeologists and climatologists including 18 German and 8 Chilean institutions. EarthShape is composed of 4 research clusters representing the process chain from weathering of substrate to deposition of eroded material. Cluster 1 explores micro-biota as the "weathering engine". Investigations in this cluster quantify different mechanisms of biogenic weathering whereby plants, fungi, and bacteria interact with rock in the production of soil. Cluster 2 explores bio-mediated redistribution of material within the weathering zone. Studies in this cluster focus on soil catenas along hill slope profiles to investigate the modification of matter along its transport path. Cluster 3 explores biotic modulation of erosion and sediment routing at the catchment scale. Investigations in this cluster explore the effects of vegetation cover on solute and sediment transport from hill slopes to the channel network. Cluster 4 explores the depositional legacy of coupled biogenic and Earthsurface systems. This cluster investigates records of vegetation-landsurface interactions in different depositional settings. A final component of EarthShape lies in the integration of results from these 4 clusters using numerical models to bridging between the diverse times scales used by different disciplines. The Chilean Coastal Cordillera between 25° and 40°S was selected to carry out this research because its north-south orientation captures a large ecological and climate gradient. This gradient ranges from hyper-arid (Atacama desert) to

Landsurface phenology (LSP) detected from satellite data characterizes seasonal dynamics of vegetation communities within a moderate or coarse resolution pixel. Its long-term variation has been widely used to indicate the biological responses to climate changes. However, few studies have focused on the influence of land disturbance on LSP variations. The wildfire is one of the most important drivers of land disturbances across the world, which shows an increasing trend during past decades. To explore the wildfire impacts on LSP, we analyzed post-fire and pre-fire LSP in two forest fire events that are Hayman Fire occurred in 2002 and Mason Fire occurred in 2005 in Colorado. Specifically, we first generated a two band enhanced vegetation index (EVI2) from MODIS daily surface reflectance product (MOD09GQ) at a spatial resolution of 250 m from 2001-2014. The time series of daily EVI2 was then used to detect the start of growing season (SOS) by applying the LSP detection algorithm based on a hybrid piecewise logistic model (HPLM-LSPD). The SOS was further separated for four levels of burn severity obtained from Monitoring Trends in Burn Severity (MTBS) maps for each fire event. The long-term SOS in the burn scars was finally deviated from surrounding areas based on land cover types. Results show that forests were mainly converted to shrubs in both fire events with some grasslands in Hayman. On average, SOS in Hayman burn scar area was advanced 11 days relative to surrounding region while it was delayed 9 days in Mason fire. The deviation also varied with the burn severity spatially. Moreover, the long-term SOS trend in the local area from 2001-2014 was significantly different with and without considerations of the fire influences. This study demonstrates that the long-term LSP SOS trend is significantly influenced by land disturbances in a local and regional scales.

During the last few decades Central Asia has experienced widespread changes in land cover and land use following the socio-economic and institutional transformations of the region catalyzed by the USSR collapse in 1991. The decade-long drought events and steadily increasing temperature regimes in the region came on top of these institutional transformations, affecting the long term and landscape scale vegetation responses. This research is based on the need to better understand the potential ecological and policy implications of climate variation and land use practices in the contexts of landscape-scale changes dynamics and variability patterns of landsurface phenology responses in Central Asia. The landsurface phenology responses -- the spatio-temporal dynamics of terrestrial vegetation derived from the remotely sensed data -- provide measurements linked to the timing of vegetation growth cycles (e.g., start of growing season) and total vegetation productivity over the growing season, which are used as a proxy for the assessment of effects of variations in environmental settings. Local and regional scale assessment of the before and after the USSR collapse vegetation response patterns in the natural and agricultural systems of the Central Asian drylands was conducted to characterize newly emerging links (since 1991) between coupled human and natural systems, e.g., socio-economic and policy drivers of altered land and water use and distribution patterns. Spatio-temporal patterns of bioclimatic responses were examined to determine how phenology is associated with temperature and precipitation in different land use types, including rainfed and irrigated agricultural types. Phenological models were developed to examine relationship between environmental drivers and effect of their altitudinal and latitudinal gradients on the broad-scale vegetation response patterns in non-cropland ecosystems of the desert, steppe, and mountainous regional landscapes of Central Asia

The marine meta-sedimentary successions in the "Barberton Mountain Land" are formed by Archean volcanic and sedimentary rocks including the oldest known impact ejecta layers on Earth. The chemical signature (high iridium concentrations, chromium isotopic ratios) of some of these up to tens of cm thick Archean spherule layers advocate that these ejecta deposits represent mainly extraterrestrial material [1]. These ejecta layers contain millimetre sized spherules that are larger and accumulated thicker layers compared to any impact ejecta layer known from Phanerozoic sediments, including the global ejecta layer of the Chicxulub impact catering event terminating the Mesozoic era of Earth's history [2]. The Archean spherule layers are interpreted as products of large impacts by 20 to >100 km diameter objects [3, 4]. Identifying traces of mega-impacts in Earth's ancient history could be of relevance for the evolution of atmosphere, biosphere, and parts of the Earth's crust during that time. In addition, recognizing global stratigraphic marker horizons is highly valuable for inter-correlating sedimentary successions between Archean cratons [5]. However estimates regarding size of the impact event and correlations between the different outcrops in the Barberton mountain land are complicated by post depositional alterations of the tectonically deformed sediments [6, 7]. The relatively fresh samples recovered from below the water table during the 2011-2012 ICDP drilling "Barberton Mountain Land" are promising samples to investigate and to discriminate primary and secondary features of these rare rocks. We plan to conduct 1) petrographic, micro-chemical and mineralogical characterization of the impact ejecta layers, 2) bulk chemical analyses of major and trace elements, and 3) LAICP- MS elemental mapping of platinum group element (PGE) distributions. and elemental analyses of moderately siderophile elements. This aims at 1) characterization of the ejecta layers, 2

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSU/NCAR MM5 V3-4 and applied on a 4-km domain for this particular application. It is recognized that a 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential landsurface forcing produced via the

In the Northern Hemisphere the 18,000 B.P. world differed strikingly from the present in the huge land-based ice sheets, reaching approximately 3 km in thickness, and in a dramatic increase in the extent of pack ice and marine-based ice sheets. In the Southern Hemisphere the most striking contrast was the greater extent of sea ice. On land, grasslands, steppes, and deserts spread at the expense of forests. This change in vegetation, together with extensive areas of permanent ice and sandy outwash plains, caused an increase in global surface albedo over modern values. Sea level was lower by at least 85 m. The 18,000 B.P. oceans were characterized by: (i) marked steepening of thermal gradients along polar frontal systems, particularly in the North Atlantic and Antarctic; (ii) an equatorward displacement of polar frontal systems; (iii) general cooling of most surface waters, with a global average of -2.3 degrees C; (iv) increased cooling and up-welling along equatorial divergences in the Pacific and Atlantic; (v) low temperatures extending equatorward along the western coast of Africa, Australia, and South America, indicating increased upwelling and advection of cool waters; and (vi) nearly stable positions and temperatures of the central gyres in the subtropical Atlantic, Pacific, and Indian oceans.

A generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data has been developed. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must depend on the viewing angle, if we are to achieve a LST accuracy of about 1 K for the whole scan swath range (+/-55.4 deg and +/-55 deg from nadir for AVHRR and MODIS, respectively) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. We obtain these coefficients from regression analysis of radiative transfer simulations, and we analyze sensitivity and error by using results from systematic radiative transfer simulations over wide ranges of surface temperatures and emissivities, and atmospheric water vapor abundance and temperatures. Simulations indicated that as atmospheric column water vapor increases and viewing angle is larger than 45 deg it is necessary to optimize the split-window method by separating the ranges of the atmospheric column water vapor and lower boundary temperature, and the surface temperature into tractable sub-ranges. The atmospheric lower boundary temperature and (vertical) column water vapor values retrieved from HIRS/2 or MODIS atmospheric sounding channels can be used to determine the range where the optimum coefficients of the split-window method are given. This new LST algorithm not only retrieves LST more accurately but also is less sensitive than viewing-angle independent LST algorithms to the uncertainty in the band emissivities of the land-surface in the split-window and to the instrument noise.

NASA and its partners are contributing important observations and expertise to the ongoing response to the April 25, 2015, magnitude 7.8 Gorkha earthquake in Nepal. The quake was the strongest to occur in that area since the 1934 Nepal-Bihar magnitude 8.0 event and caused significant regional damage and a humanitarian crisis. Scientists with the Advanced Rapid Imaging and Analysis project (ARIA), a collaboration between NASA's Jet Propulsion Laboratory, Pasadena, California, and the California Institute of Technology in Pasadena, analyzed interferometric synthetic aperture radar images from the European Union's Copernicus Sentinel-1A satellite, operated by the European Space Agency and also available from the Alaska Satellite Facility (https://www.asf.alaska.edu), to calculate a map of the deformation of Earth's surface caused by the quake. This false-color map shows the amount of permanent surface movement caused almost entirely by the earthquake, as viewed by the satellite, during a 12-day interval between two Sentinel-1 images acquired on April 17 and April 29, 2015. In the map, surface displacements are seen as color contours (or "fringes"), where each color cycle represents 8 inches (20 centimeters) of surface motion. The contours show the land around Kathmandu has moved upward by more than 40 inches (1 meter). Areas without the color contours have snow or heavy vegetation that affects the radar measurements. Scientists use these maps to build detailed models of the fault and associated land movements to better understand the impact on future earthquake activity. The background image is from Google Earth. The map contains Copernicus data (2015). http://photojournal.jpl.nasa.gov/catalog/PIA19535

The southeastern United States has high landscape heterogeneity, withheavily managed forestlands, highly developed agriculture lands, and multiple metropolitan areas. Human activities are transforming and altering land patterns and structures in both negative and positive manners. A land-use map for at the greater scale is a heavy computation task but is critical to most landowners, researchers, and decision makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating the classification maps at the regional scale: the necessity of large training point sets and the expensive computation cost-in terms of both money and time-in classifier modeling. Volunteered Geographic Information (VGI) opens a new era in mapping and visualizing our world, where the platform is open for collecting valuable georeferenced information by volunteer citizens, and the data is freely available to the public. As one of the most well-known VGI initiatives, OpenStreetMap (OSM) contributes not only road network distribution, but also the potential for using this data to justify land cover and land use classifications. Google Earth Engine (GEE) is a platform designed for cloud-based mapping with a robust and fast computing power. Most large scale and national mapping approaches confuse "land cover" and "land-use", or build up the land-use database based on modeled land cover datasets. Unlike most other large-scale approaches, we distinguish and differentiate land-use from land cover. By focusing our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach is developed by incorporating the OpenstreepMap dataset into Earth observation remote sensing imageries instead of the often-used land cover base maps.

We introduce a novel Earth-like planet surface temperature model (ESTM) for habitability studies based on the spatial-temporal distribution of planetary surface temperatures. The ESTM adopts a surface energy balance model (EBM) complemented by: radiative-convective atmospheric column calculations, a set of physically based parameterizations of meridional transport, and descriptions of surface and cloud properties more refined than in standard EBMs. The parameterization is valid for rotating terrestrial planets with shallow atmospheres and moderate values of axis obliquity (ɛ ≲ 45{}^\\circ ). Comparison with a 3D model of atmospheric dynamics from the literature shows that the equator-to-pole temperature differences predicted by the two models agree within ≈ 5 K when the rotation rate, insolation, surface pressure and planet radius are varied in the intervals 0.5≲ {Ω }/{{{Ω }}\\oplus }≲ 2, 0.75≲ S/{{S}\\circ }≲ 1.25, 0.3≲ p/(1 bar)≲ 10, and 0.5≲ R/{{R}\\oplus }≲ 2, respectively. The ESTM has an extremely low computational cost and can be used when the planetary parameters are scarcely known (as for most exoplanets) and/or whenever many runs for different parameter configurations are needed. Model simulations of a test-case exoplanet (Kepler-62e) indicate that an uncertainty in surface pressure within the range expected for terrestrial planets may impact the mean temperature by ˜ 60 K. Within the limits of validity of the ESTM, the impact of surface pressure is larger than that predicted by uncertainties in rotation rate, axis obliquity, and ocean fractions. We discuss the possibility of performing a statistical ranking of planetary habitability taking advantage of the flexibility of the ESTM.

Rapidly expanding human activities have profoundly affected various biophysical and biogeochemical processes of the Earth system over a broad range of scales, and freshwater systems are now amongst the most extensively altered ecosystems. In this study, we examine the human-induced changes in landsurface water and energy balances and the associated climate impacts using a coupled hydrological-climate model framework which also simulates the impacts of human activities on the water cycle. We present three sets of analyses using the results from two model versions—one with and the other without considering human activities; both versions are run in offline and coupled mode resulting in a series of four experiments in total. First, we examine climate and human-induced changes in regional water balance focusing on the widely debated issue of the desiccation of the Aral Sea in central Asia. Then, we discuss the changes in surface temperature as a result of changes in landsurface energy balance due to irrigation over global and regional scales. Finally, we examine the global and regional climate impacts of increased atmospheric water vapor content due to irrigation. Results indicate that the direct anthropogenic alteration of river flow in the Aral Sea basin resulted in the loss of 510 km3 of water during the latter half of the twentieth century which explains about half of the total loss of water from the sea. Results of irrigation-induced changes in surface energy balance suggest a significant surface cooling of up to 3.3 K over 1° grids in highly irrigated areas but a negligible change in landsurface temperature when averaged over sufficiently large global regions. Results from the coupled model indicate a substantial change in 2 m air temperature and outgoing longwave radiation due to irrigation, highlighting the non-local (regional and global) implications of irrigation. These results provide important insights on the direct human alteration of landsurface

Objective: The objective of this proposal is to provide a routine landsurface modeling and data assimilation capability for GPM in order to provide global landsurface states that are necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in GPM, is sensitive to landsurface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. Therefore, providing a robust capability to routinely provide these critical land states is essential to support GPM-era physical retrieval algorithms over land.

Weather information is an important asset for NASA's Constellation Program in developing the next generation space transportation system to fly to the International Space Station, the Moon and, eventually, to Mars. Weather conditions can affect vehicle safety and performance during multiple mission phases ranging from pre-launch ground processing of the Ares vehicles to landing and recovery operations, including all potential abort scenarios. Meteorological analysis is art important contributor, not only to the development and verification of system design requirements but also to mission planning and active ground operations. Of particular interest are the surface weather conditions at both nominal and abort landing sites for the manned Orion capsule. Weather parameters such as wind, rain, and fog all play critical roles in the safe landing of the vehicle and subsequent crew and vehicle recovery. The Marshall Space Flight Center (MSFC) Natural Environments Branch has been tasked by the Constellation Program with defining the natural environments at potential landing zones. This paper wiI1 describe the methodology used for data collection and quality control, detail the types of analyses performed, and provide a sample of the results that cab be obtained.

The Satellite Application Facility on LandSurface Analysis (LSA-SAF, http://landsaf.ipma.pt) has been providing landsurface temperature (LST) estimates using SEVIRI/MSG on an operational basis since 2006. The LSA-SAF service has since been extended to provide a wide range of satellite-based quantities over landsurfaces, such as emissivity, albedo, radiative fluxes, vegetation state, evapotranspiration, and fire-related variables. Being based on infra-red measurements, the SEVIRI/MSG LST product is limited to clear-sky pixels only. Several all-weather LST products have been proposed by the scientific community either based on microwave observations or using Soil-Vegetation-Atmosphere Transfer models to fill the gaps caused by clouds. The goal of this work is to provide a nearly gap-free operational all-weather LST product and compare these approaches. In order to estimate evapotranspiration and turbulent energy fluxes, the LSA-SAF solves the surface energy budget for each SEVIRI pixel, taking into account the physical and physiological processes occurring in vegetation canopies. This task is accomplished with an adapted SVAT model, which adopts some formulations and parameters of the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) model operated at the European Center for Medium-range Weather Forecasts (ECMWF), and using: 1) radiative inputs also derived by LSA-SAF, which includes surface albedo, down-welling fluxes and fire radiative power; 2) a land-surface characterization obtained by combining the ECOCLIMAP database with both LSA-SAF vegetation products and the H(ydrology)-SAF snow mask; 3) meteorological fields from ECMWF forecasts interpolated to SEVIRI pixels, and 4) soil moisture derived by the H-SAF and LST from LSA-SAF. A byproduct of the SVAT model is surface skin temperature, which is needed to close the surface energy balance. The model skin temperature corresponds to the radiative temperature of the interface between soil and atmosphere

The S-193 Skylab radar altimeter was operated in a round-the-world pass on Jan. 31, 1974. The main purpose of this experiment was to test and 'measure' the variation of the sea surface topography using the Goddard Space Flight Center (GSFC) geoid model as a reference. This model is based upon 430,000 satellite and 25,000 ground gravity observations. Variations of the sea surface on the order of -40 to +60 m were observed along this pass. The 'computed' and 'measured' sea surfaces have an rms agreement on the order of 7 m. This is quite satisfactory, considering that this was the first time the sea surface has been observed directly over a distance of nearly 35,000 km and compared to a computed model. The Skylab orbit for this global pass was computed using the Goddard Earth Model (GEM 6) and S-band radar tracking data, resulting in an orbital height uncertainty of better than 5 m over one orbital period.

The impact of landsurface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, mitigating the coupling shock and possibly increasing the model predictive skill in the ocean. In fact, in a few regions characterized by strong air-sea coupling, the atmosphere initial condition affects the forecast skill for several months. In particular, the ENSO region, the eastern tropical Atlantic and the North Pacific benefit significantly from the atmosphere initialization. On mainland, the impact of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of landsurface initialization affects the forecast skill in the following lead seasons. The winter forecast in the high latitude plains of Siberia and Canada benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region, in central Asia and Australia. However, initialization through landsurface reanalysis does not systematically guarantee an enhancement of the predictive skill: the quality of the forecast is sometimes higher for the non-constrained model. Overall, the introduction of a realistic initialization of landsurface and atmosphere substantially increases skill and accuracy. However, further developments in the operating procedure for landsurface initialization are required for more accurate seasonal forecasts.

This report summarizes the software products and system architectures developed by Lockheed Martin in support of the Low Visibility Landing and Surface Operations (LVLASO) program at NASA Langley Research Center. It presents an overview of the technical aspects, capabilities, and system integration issues associated with an integrated display system (IDS) that collects, processes and presents information to an aircraft flight crew during all phases of landing, roll-out, turn-off, inbound taxi, outbound taxi and takeoff. Communications hardware, drivers, and software provide continuous real-time data at varying rates and from many different sources to the display programs for presentation on a head-down display (HDD) and/or a head-up display (HUD). An electronic moving map of the airport surface is implemented on the HDD which includes the taxi route assigned by air traffic control, a text messaging system, and surface traffic and runway status information. Typical HUD symbology for navigation and control of the aircraft is augmented to provide aircraft deceleration guidance after touchdown to a pilot selected exit and taxi guidance along the route assigned by ATC. HUD displays include scene-linked symbolic runways, runway exits and taxiways that are conformal with the actual locations on the airport surface. Display formats, system architectures, and the various IDS programs are discussed.

Landsurface temperature (Ts) is an important element to measure the state of terrestrial ecosystems and to study surface energy budgets. In support of the land cover/land use change-related international program MAIRS (Monsoon Asia Integrated Regional Study), we have collected global monthly Ts measured by MODIS since the beginning of the missions. The MODIS Ts time series have approximately 11 years of data from Terra since 2000 and approximately 9 years of data from Aqua since 2002, which makes possible to study the recent climate, such as trend. In this study, monthly climatology from two platforms are calculated and compared with that from AIRS. The spatial patterns of Ts trends are accessed, focusing on the Eurasia region. Furthermore, MODIS Ts trends are compared with those from AIRS and NASA's atmospheric assimilation model, MERRA (Modern Era Retrospective-analysis for Research and Applications). The preliminary results indicate that the recent 8-year Ts trend shows an oscillation-type spatial variation over Eurasia. The pattern is consistent for data from MODIS, AIRS, and MERRA, with the positive center over Eastern Europe, and the negative center over Central Siberia. The calculated climatology and anomaly of MODIS Ts will be integrated into the online visualization system, Giovanni, at NASA GES DISC for easy use by scientists and general public.

Investigating and evaluating physical-chemical-biological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation. Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presentedmore » to demonstrate the implementation details and system innovations.« less

Investigating and evaluating physical-chemical-biological processes within an Earth system model (EMS) can be very challenging due to the complexity of both model design and software implementation. A virtual observation system (VOS) is presented to enable interactive observation of these processes during system simulation. Based on advance computing technologies, such as compiler-based software analysis, automatic code instrumentation, and high-performance data transport, the VOS provides run-time observation capability, in-situ data analytics for Earth system model simulation, model behavior adjustment opportunities through simulation steering. A VOS for a terrestrial land model simulation within the Accelerated Climate Modeling for Energy model is also presentedmore » to demonstrate the implementation details and system innovations.« less

We investigate the spatiotemporal temperature variability for several gridded instrumental and climate model data sets. The temporal variability is analysed by estimating the power spectral density and studying the differences between local and global temperatures, land and sea, and among local temperature records at different locations. The spatiotemporal correlation structure is analysed through cross-spectra that allow us to compute frequency-dependent spatial autocorrelation functions (ACFs). Our results are then compared to theoretical spectra and frequency-dependent spatial ACFs derived from a fractional stochastic-diffusive energy balance model (FEBM). From the FEBM we expect both local and global temperatures to have a long-range persistent temporal behaviour, and the spectral exponent (β) is expected to increase by a factor of two when going from local to global scales. Our comparison of the average local spectrum and the global spectrum shows good agreement with this model, although the FEBM has so far only been studied for a pure land planet and a pure ocean planet, respectively, with no seasonal forcing. Hence it cannot capture the substantial variability among the local spectra, in particular between the spectra for land and sea, and for equatorial and non-equatorial temperatures. Both models and observation data show that land temperatures in general have a low persistence, while sea surface temperatures show a higher, and also more variable degree of persistence. Near the equator the spectra deviate from the power-law shape expected from the FEBM. Instead we observe large variability at time scales of a few years due to ENSO, and a flat spectrum at longer time scales, making the spectrum more reminiscent of that of a red noise process. From the frequency-dependent spatial ACFs we observe that the spatial correlation length increases with increasing time scale, which is also consistent with the FEBM. One consequence of this is that longer

Observations of landsurface temperature (LST) are crucial for the monitoring of surface energy fluxes from satellite. Methods that require high temporal resolution LST observations (e.g., from geostationary orbit) can be difficult to apply globally because several geostationary sensors are required...

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in landsurface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of landsurface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear

The dark current and noise characteristics of the Earth Observing-1 Advanced Land Imager measured during ground calibration at MIT Lincoln Laboratory are presented. Data were collected for the nominal focal plane operating temperature of 220 K as well as supplemental operating temperatures (215 and 225 K). Dark current baseline values are provided, and noise characterization includes the evaluation of white, coherent, low frequency, and high frequency components. Finally, anomalous detectors, characterized by unusual dark current, noise, gain, or cross-talk properties are investigated.

Evaluation of atmosphere, ocean, sea ice, and landsurface models is an important step in identifying deficiencies in Earth system models and developing improved estimates of future change. For the landsurface and carbon cycle, the design of an open-source system has been an important objective of the International Land Model Benchmarking (ILAMB) project. Here we evaluated CMIP5 and CLM models using a benchmarking system that enables users to specify models, data sets, and scoring systems so that results can be tailored to specific model intercomparison projects. Our scoring system used information from four different aspects of global datasets, including climatological mean spatial patterns, seasonal cycle dynamics, interannual variability, and long-term trends. Variable-to-variable comparisons enable investigation of the mechanistic underpinnings of model behavior, and allow for some control of biases in model drivers. Graphics modules allow users to evaluate model performance at local, regional, and global scales. Use of modular structures makes it relatively easy for users to add new variables, diagnostic metrics, benchmarking datasets, or model simulations. Diagnostic results are automatically organized into HTML files, so users can conveniently share results with colleagues. We used this system to evaluate atmospheric carbon dioxide, burned area, global biomass and soil carbon stocks, net ecosystem exchange, gross primary production, ecosystem respiration, terrestrial water storage, evapotranspiration, and surface radiation from CMIP5 historical and ESM historical simulations. We found that the multi-model mean often performed better than many of the individual models for most variables. We plan to publicly release a stable version of the software during fall of 2014 that has landsurface, carbon cycle, hydrology, radiation and energy cycle components.

A comprehensive series of global datasets for land-atmosphere models has been collected, formatted to a common grid, and released on a set of CD-ROMs. This paper describes the motivation for and the contents of the dataset. In June of 1992, an interdisciplinary earth science workshop was convened in Columbia, Maryland, to assess progress in land-atmosphere research, specifically in the areas of models, satellite data algorithms, and field experiments. At the workshop, representatives of the land-atmosphere modeling community defined a need for global datasets to prescribe boundary conditions, initialize state variables, and provide near-surface meteorological and radiative forcings for their models.more » The International Satellite LandSurface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment, worked with the Distributed Active Archive Center of the National Aeronautics and Space Administration Goddard Space Flight Center to bring the required datasets together in a usable format. The data have since been released on a collection of CD-ROMs. The datasets on the CD-ROMs are grouped under the following headings: vegetation; hydrology and soils; snow, ice, and oceans; radiation and clouds; and near-surface meteorology. All datasets cover the period 1987-88, and all but a few are spatially continuous over the earth`slandsurface. All have been mapped to a common 1{degree} x 1{degree} equal-angle grid. The temporal frequency for most of the datasets is monthly. A few of the near-surface meteorological parameters are available both as six-hourly values and as monthly means. 26 refs., 8 figs., 2 tabs.« less

Soil moisture, surface temperature and vegetation are variables that play an important role in our environment. There is growing demand for accurate estimation of these geophysical parameters for the research of global climate models (GCMs), weather, hydrological and flooding models, and for the application to agricultural assessment, land cover change, and a wide variety of other uses that meet the needs for the study of our environment. The different studies covered in this dissertation evaluate the capabilities and limitations of microwave passive sensors to monitor landsurface dynamics. The first study evaluates the 19 GHz channel of the SSM/I instrument with a radiative transfer model and in situ datasets from the Illinois stations and the Oklahoma Mesonet to retrieve landsurface temperature and surface soil moisture. The surface temperatures were retrieved with an average error of 5 K and the soil moisture with an average error of 6%. The results show that the 19 GHz channel can be used to qualitatively predict the spatial and temporal variability of surface soil moisture and surface temperature at regional scales. In the second study, in situ observations were compared with sensor observations to evaluate aspects of low and high spatial resolution at multiple frequencies with data collected from the Southern Great Plains Experiment (SGP99). The results showed that the sensitivity to soil moisture at each frequency is a function of wavelength and amount of vegetation. The results confirmed that L-band is more optimal for soil moisture, but each sensor can provide soil moisture information if the vegetation water content is low. The spatial variability of the emissivities reveals that resolution suffers considerably at higher frequencies. The third study evaluates C- and X-bands of the AMSR-E instrument. In situ datasets from the Soil Moisture Experiments (SMEX03) in South Central Georgia were utilized to validate the AMSR-E soil moisture product and to

Landsurface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods. PMID:27879844

Landsurface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.

Researchers mapped the surface materials at the Viking landing sites on Mars to gain a better understanding of the materials and rock populations at the sites and to provide information for future exploration. The maps extent to about 9 m in front of each lander and are about 15 m wide - an area comparable to the area of a pixel in high resolution Viking Orbiter images. The maps are divided into the near and far fields. Data for the near fields are from 1/10 scale maps, umpublished maps, and lander images. Data for the far fields are from 1/20 scale contour maps, contoured lander camera mosaics, and lander images. Rocks are located on these maps using stereometric measurements and the contour maps. Frequency size distribution of rocks and the responses of soil-like materials to erosion by engine exhausts during landings are discussed.

The landsurface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the landsurface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment landsurface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

The sharp increase of the man-induced pressure on the environment and hence the need to predict and monitor natural anomalies makes global monitoring of the ecosphere of planet Earth an issue of vital importance. The notion of the ecosphere covers three basic shells closely interacting with each other: the near-Earth space, the atmosphere and the Earthsurface. In the near-Earth space (covering 100 to 2000 km altitudes) the primary objects of monitoring are: functioning artificial space objects, the fragments of their constructions or space rubbish (which by estimation amounts to 3.5 million pieces including 30,000 to 70,000 objects having dimensions sufficient for heavy damaging or even destroying functioning space objects) and objects of space origin (asteroids, meteorites and comets) whose trajectories come closely enough to the Earth. Maximum concentrations of space rubbish observed on orbits with altitudes of 800, 1000 and 1500 km and inclinations of 60 to 100 deg. are related in the first place to spacecraft launch requirements. Taking into account the number of launches implemented by different countries in the framework of their own space programs the probability of collision of functioning spacecraft with space rubbish may be estimation increase from (1.5 - 3.5)% at present to (15 - 40)% by 2020. Besides, registration of space radiation flow intensity and the solar activity is no less important in this space area. Subject to control in the atmosphere are time and space variations in temperature fields, humidity, tracing gas concentrations, first of all ozone and greenhouse gases, the state of the cloud cover, wind velocity, etc. The range of objects to be under environmental management of Earthsurface is just as diverse and essentially should include the state of the surface and the near-surface layer of seas and oceans, internal reservoirs, the cryosphere and the landsurface along with vegetation cover, natural resources and human activities. No matter

Weather information is an important asset for NASA's Constellation Program in developing the next generation space transportation system to fly to the International Space Station, the Moon and, eventually, to Mars. Weather conditions can affect vehicle safety and performance during multiple mission phases ranging from pre-launch ground processing to landing and recovery operations, including all potential abort scenarios. Meteorological analysis is an important contributor, not only to the development and verification of system design requirements but also to mission planning and active ground operations. Of particular interest are the surface atmospheric conditions at both nominal and abort landing sites for the manned Orion capsule. Weather parameters such as wind, rain, and fog all play critical roles in the safe landing of the vehicle and subsequent crew and vehicle recovery. The Marshall Space Flight Center Natural Environments Branch has been tasked by the Constellation Program with defining the natural environments at potential landing zones. Climatological time series of operational surface weather observations are used to calculate probabilities of occurrence of various sets of hypothetical vehicle constraint thresholds, Data are available for numerous geographical locations such that statistical analysis can be performed for single sites as well as multiple-site network configurations. Results provide statistical descriptions of how often certain weather conditions are observed at the site(s) and the percentage that specified criteria thresholds are matched or exceeded. Outputs are tabulated by month and hour of day to show both seasonal and diurnal variation. This paper will describe the methodology used for data collection and quality control, detail the types of analyses performed, and provide a sample of the results that can be obtained,

Information related to landsurface phenology is important for a variety of applications. For example, phenology is widely used as a diagnostic of ecosystem response to global change. In addition, phenology influences seasonal scale fluxes of water, energy, and carbon between the landsurface and atmosphere. Increasingly, the importance of phenology for studies of habitat and biodiversity is also being recognized. While many data sets related to plant phenology have been collected at specific sites or in networks focused on individual plants or plant species, remote sensing provides the only way to observe and monitor phenology over large scales and at regular intervals. The MODIS Global Land Cover Dynamics Product was developed to support investigations that require regional to global scale information related to spatiotemporal dynamics in landsurface phenology. Here we describe the Collection 5 version of this product, which represents a substantial refinement relative to the Collection 4 product. This new version provides information related to landsurface phenology at higher spatial resolution than Collection 4 (500-m vs. 1-km), and is based on 8-day instead of 16-day input data. The paper presents a brief overview of the algorithm, followed by an assessment of the product. To this end, we present (1) a comparison of results from Collection 5 versus Collection 4 for selected MODIS tiles that span a range of climate and ecological conditions, (2) a characterization of interannual variation in Collections 4 and 5 data for North America from 2001 to 2006, and (3) a comparison of Collection 5 results against ground observations for two forest sites in the northeastern United States. Results show that the Collection 5 product is qualitatively similar to Collection 4. However, Collection 5 has fewer missing values outside of regions with persistent cloud cover and atmospheric aerosols. Interannual variability in Collection 5 is consistent with expected ranges of

The landsurface temperature (LST) is an extremely significant parameter in order to understand the processes of energetic interactions between Earth's surface and atmosphere. This knowledge is significant for various environmental research questions, particularly with regard to the recent climate change. This study shows an innovative approach to retrieve landsurface emissivity (LSE) and LST by using thermal infrared (TIR) data from satellite sensors, such as SEVIRI and AATSR. So far there are no methods to derive LSE/LST particularly in areas of highly dynamic emissivity changes. Therefore especially for regions with large surface temperature amplitude in the diurnal cycle such as bare and uneven soil surfaces but also for regions with seasonal changes in vegetation cover including various surface areas such as grassland, mixed forests or agricultural land different methods were investigated to identify the most appropriate one. The LSE is retrieved by using the day/night Temperature-Independent Spectral Indices (TISI) method, and the Generalised Split-Window (GSW) method is used to retrieve the LST. Nevertheless different GSW algorithms show that equal LSEs lead to large LST differences. Additionally LSE is also measured using a NDVI-based threshold method (NDVITHM) to distinguish between soil, dense vegetation cover and pixel composed of soil and vegetation. The data used for this analysis were derived from MODIS TIR. The analysis is implemented with IDL and an intercomparison is performed to determine the most effective methods. To compensate temperature differences between derived and ground truth data appropriate correction terms by comparing derived LSE/LST data with ground-based measurements are developed. One way to calibrate LST retrievals is by comparing the canopy leaf temperature of conifers derived from TIR data with the surrounding air temperature (e.g. from synoptic stations). Prospectively, the derived LSE/LST data become validated with near

Long-term space operations that require exposure of material to the low earth orbit (LEO) environment must take into account the effects of this highly oxidative atmosphere on material properties and the possible contamination of the spacecraft surroundings. Ground-based laboratory experiments at Los Alamos using a newly developed hyperthermal atomic oxygen (AO) source have shown that not only are hydrocarbon based materials effected but that inorganic materials such as MoS2 are also oxidized and that thin protective coatings such as Al2O3 can be breached, producing oxidation of the underlying substrate material. Gas-phase reaction products, such as SO2 from oxidation of MoS2 and CO and CO2 from hydrocarbon materials, have been detected and have consequences in terms of spacecraft contamination. Energy loss through gas-surface collisions causing spacecraft drag has been measured for a few select surfaces and has been found to be highly dependent on the surface reactivity.

The characteristics of solar energy arriving at the surface of the Earth are defined and the history of solar measurements in the United States presented. Radiation and meteorological measurements being made at solar energy meteorological research and training sites and calibration procedures used there are outlined. Data illustrating the annual variation in daily solar radiation at Ann Arbor, Michigan and the diurnal variation in radiation at Albuquerque, New Mexico are presented. Direct normal solar radiation received at Albuquerque is contrasted with that received at Maynard, Massachusetts. Average measured global radiation for a period of one year for four locations under clear skies, 50% cloud cover, and 100% cloud cover is given and compared with the solar radiation at the top of the atmosphere. The May distribution of mean daily direct solar radiation and mean daily global solar radiation over the United States is presented. The effects of turbidity on the direct and circumsolar radiation are shown.

A series of numerical experiments are carried out to investigate the sensitivity of a landfalling monsoon depression to landsurface conditions using the Weather Research and Forecasting (WRF) model. Results suggest that precipitation is largely modulated by moisture influx and precipitation efficiency. Three cloud microphysical schemes (WSM6, WDM6, and Morrison) are examined, and Morrison is chosen for assessing the landsurface-precipitation feedback analysis, owing to better precipitation forecast skills. It is found that increased soil moisture facilitates Moisture Flux Convergence (MFC) with reduced moisture influx, whereas a reduced soil moisture condition facilitates moisture influx but not MFC. A higher Moist Static Energy (MSE) is noted due to increased evapotranspiration in an elevated moisture scenario which enhances moist convection. As opposed to moist surface, sensible heat dominates in a reduced moisture scenario, ensued by an overall reduction in MSE throughout the Planetary Boundary Layer (PBL). Stability analysis shows that Convective Available Potential Energy (CAPE) is comparable in magnitude for both increased and decreased moisture scenarios, whereas Convective Inhibition (CIN) shows increased values for the reduced moisture scenario as a consequence of drier atmosphere leading to suppression of convection. Simulations carried out with various fixed soil moisture levels indicate that the overall precipitation features of the storm are characterized by initial soil moisture condition, but precipitation intensity at any instant is modulated by soil moisture availability. Overall results based on this case study suggest that antecedent soil moisture plays a crucial role in modulating precipitation distribution and intensity of a monsoon depression.

A significant progress has been made in TIR instrumentation which is required to establish the spectral BRDF/emissivity knowledge base of land-surface materials and to validate the land-surface temperature (LST) algorithms. The SIBRE (spectral Infrared Bidirectional Reflectance and Emissivity) system and a TIR system for measuring spectral directional-hemispherical emissivity have been completed and tested successfully. Optical properties and performance features of key components (including spectrometer, and TIR source) of these systems have been characterized by integrated use of local standards (blackbody and reference plates). The stabilization of the spectrometer performance was improved by a custom designed and built liquid cooling system. Methods and procedures for measuring spectral TIR BRDF and directional-hemispheric emissivity with these two systems have been verified in sample measurements. These TIR instruments have been used in the laboratory and the field, giving very promising results. The measured spectral emissivities of water surface are very close to the calculated values based on well established water refractive index values in published papers. Preliminary results show that the TIR instruments can be used for validation of the MODIS LST algorithm in homogeneous test sites. The beta-3 version of the MODIS LST software is being prepared for its delivery scheduled in the early second half of this year.

The model Soil-Litter-Iso (SLI) calculates coupled heat and water transport in soil. It was recently implemented into the Australian landsurface model CABLE, which is the land component of the Australian Community Climate and Earth System Simulator (ACCESS). Here we extended SLI to include accurate freeze-thaw processes in the soil and snow. SLI provides thence an implicit solution of the energy and water balances of soil and snow as a standalone model and within CABLE. The enhanced SLI was tested extensively against theoretical formulations, laboratory experiments, field data, and satellite retrievals. The model performed well for all experiments at wide-ranging temporal and spatial scales. SLI melts snow faster at the end of the cold season compared to observations though because there is no subgrid variability within SLI given by the implicit, coupled solution of energy and water. Combined CABLE-SLI shows very realistic dynamics and extent of permafrost on the Northern hemisphere. It illustrated, however, also the limits of possible comparisons between large-scale landsurface models and local permafrost observations. CABLE-SLI exhibits the same patterns of snow depth and snow water equivalent on the Northern hemisphere compared to satellite-derived observations but quantitative comparisons depend largely on the given meteorological input fields. Further extension of CABLE-SLI with depth-dependence of soil carbon will allow realistic projections of the development of permafrost and frozen carbon stocks in a changing climate.

Land-surface models (LSMs) are crucial components of the Earth System Models (ESMs) which are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. In this study, JULES is automatically differentiated using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed, to search for locally optimum parameter sets by calibrating against observations. We present an introduction to the adJULES system and demonstrate its ability to improve the model-data fit using eddy covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the 5 Plant Functional Types (PFTS) in JULES. The optimised PFT-specific parameters improve the performance of JULES over 90% of the FLUXNET sites used in the study. These reductions in error are shown and compared to reductions found due to site-specific optimisations. Finally, we show that calculation of the 2nd derivative of JULES allows us to produce posterior probability density functions of the parameters and how knowledge of parameter values is constrained by observations.

Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through landsurface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to downscale LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a landsurface model which simulates prior subpixel temperatures. First, the GPF downscaling scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for downscaling LST satellite products.

Landsurface phenology (LSP), the study of seasonal dynamics of vegetated landsurfaces from remote sensing, is a key indicator of global change, that both responds to and influences weather and climate. The effects of climatic changes on LSP depend on the relative importance of climatic constraints in specific regions—which are not well understood at global scale. Understanding the climatic constraints that underlie LSP is crucial for explaining climate change effects on global vegetation phenology. We used a combination of modelled and remotely-sensed vegetation activity records to quantify the interplay of three climatic constraints on landsurface phenology (namely minimum temperature, moisture availability, and photoperiod), as well as the dynamic nature of these constraints. Our study examined trends and the relative importance of the three constrains at the start and the end of the growing season over eight global environmental zones, for the past three decades. Our analysis revealed widespread shifts in the relative importance of climatic constraints in the temperate and boreal biomes during the 1982-2011 period. These changes in the relative importance of the three climatic constraints, which ranged up to 8% since 1982 levels, varied with latitude and between start and end of the growing season. We found a reduced influence of minimum temperature on start and end of season in all environmental zones considered, with a biome-dependent effect on moisture and photoperiod constraints. For the end of season, we report that the influence of moisture has on average increased for both the temperate and boreal biomes over 8.99 million km2. A shifting relative importance of climatic constraints on LSP has implications both for understanding changes and for improving how they may be modelled at large scales.

Land cover change across the Northern Great Plains of North America over the past three decades has been driven by changes in agricultural management (conservation tillage; irrigation), government incentives (Conservation Reserve Program; subsidies to grain-based ethanol), crop varieties (cold-hardy soybean), and market dynamics (increasing world demand). Climate change across the Northern Great Plains over the past three decades has been evident in trends toward earlier warmth in the spring and a longer frost-free season. Together these land and climate changes induce shifts in local and regional landsurface phenologies (LSPs). Any significant shift in LSP may correspond to a significant shift in evapotranspiration, with consequences for regional hydrometeorology. We explored possible future scenarios involving land use and climate change in six steps. First, we defined the nominal draw areas of current and future biorefineries in North Dakota, South Dakota, Nebraska, Minnesota, and Iowa and masked those land cover types within the draw areas that were unlikely to change to agricultural use (open water, settlements, forests, etc.). Second, we estimated the proportion of corn and soybean remaining within the masked draw areas using MODIS-derived crop maps. Third, in each draw area, we modified LSPs to simulate crop changes for a control and two treatment scenarios. In the control, we used LSP profiles identified from MODIS Collection 5 NBAR data. In one treatment, we increased the proportion of tallgrass LSPs in the draw areas to represent widespread cultivation of a perennial cellulosic crop, like switchgrass. In a second treatment, we increased the proportion of corn LSPs in the draw areas to represent increased corn cultivation. Fourth, we characterized the seasonal progression of the thermal regime associated with the LSP profiles using MODIS LandSurface Temperature (LST) products. Fifth, we modeled the LSP profile as a quadratic function of accumulated

The Office of Water Prediction (OWP) produces high-value water supply and flood risk planning information through the use of operational landsurface modeling. Improvements in diagnosing frozen precipitation density will benefit the NWS's meteorological and hydrological services by refining estimates of a significant and vital input into landsurface models. A current common practice for handling the density of snow accumulation in a landsurface model is to use a standard 10:1 snow-to-liquid-equivalent ratio (SLR). Our research findings suggest the possibility of a more skillful approach for assessing the spatial variability of precipitation density. We developed a 30-year SLR climatology for the coterminous US from version 3.22 of the Daily Global Historical Climatology Network - Daily (GHCN-D) dataset. Our methods followed the approach described by Baxter (2005) to estimate mean climatological SLR values at GHCN-D sites in the US, Canada, and Mexico for the years 1986-2015. In addition to the Baxter criteria, the following refinements were made: tests were performed to eliminate SLR outliers and frequent reports of SLR = 10, a linear SLR vs. elevation trend was fitted to station SLR mean values to remove the elevation trend from the data, and detrended SLR residuals were interpolated using ordinary kriging with a spherical semivariogram model. The elevation values of each station were based on the GMTED 2010 digital elevation model and the elevation trend in the data was established via linear least squares approximation. The ordinary kriging procedure was used to interpolate the data into gridded climatological SLR estimates for each calendar month at a 0.125 degree resolution. To assess the skill of this climatology, we compared estimates from our SLR climatology with observations from the GHCN-D dataset to consider the potential use of this climatology as a first guess of frozen precipitation density in an operational landsurface model. The difference in

Experimental studies of the dynamics of morphological and functional state of the diverse biosystems (microflora, plant Maranta leuconeura Â«FascinatorÂ», cell cultures, human peripheral blood, the human body ) have shown that geocosmical agents modulated the functional state of biological systems Belisheva 2006; Belisheva et all 2007 ) . First time on the experimental data showed the importance of the increase in the fluxes of solar cosmic rays (CRs ) with high energies (Belisheva et all 2002; 2012; Belisheva, Lammer, Biernat, 2004) and galactic cosmic ray variations (Belisheva et al, 2005; 2006; Vinnichenko Belisheva, 2009 ) near the Earthsurface for the functional state of biosystems. The evidence of the presence of the particles with high bioeffectiveness in the secondary cosmic rays was obtained by simulating the particle cascades in the atmosphere, performed by using Geant4 (Planetocosmics, based on the Monte Carlo code (Maurchev et al, 2011), and experimental data, where radiobiological effects of cosmic rays were revealed. Modeling transport of solar protons through the Earth's atmosphere, taking into account the angular and energy distributions of secondary particles in different layers of the atmosphere, allowed us to estimate the total neutron flux during three solar proton events, accompanied by an increase in the intensity of the nucleon component of secondary cosmic rays - Ground Level Enhancement GLE (43, 44, 45) in October 1989 (19, 22, 24 October). The results obtained by simulation were compared with the data of neutron monitors and balloon measurements made during solar proton events. Confirmation of the neutron fluxes near the Earthsurface during the GLE (43, 44, 45) were obtained in the experiments on the cellular cultures (Belisheva et al. 2012). A direct evidence of biological effects of CR has been demonstrated in experiments with three cellular lines growing in culture during three events of Ground Level Enhancement (GLEs) in the

Earth's surface topography is controlled by isostatically compensated density variations within the lithosphere, but dynamic topography - i.e. the topography due to adjustment of surface to mantle convection - is an important component, specially at a global scale. In order to separate these two components it is fundamental to estimate crustal and mantle density structure and rheological properties. Usually, crustal density is constrained from interpretation of available seismic data (mostly VP profiles) based on empirical relationships such those in Brocher [2005]. Mantle density structure is inferred from seismic tomography models. Constant coefficients are used to interpret seismic velocity anomalies in density anomalies. These simplified methods are unable to model the effects that pressure and temperature variations have on mineralogical assemblage and physical properties. Our approach is based on a multidisciplinary method that involves geophysical observables, mineral physics constraints, and petrological data. Mantle density is based on the thermal interpretation of global seismic tomography models assuming various compositional structures, as in Cammarano et al. [2011]. We further constrain the top 150 km by including heat-flow data and considering the thermal evolution of the oceanic lithosphere. Crustal density is calculated as in Guerri and Cammarano [2015] performing thermodynamic modeling of various average chemical compositions proposed for the crust. The modeling, performed with the code PerpleX [Connolly, 2005], relies on the thermodynamic dataset from Holland and Powell [1998]. Compressional waves velocity and crustal layers thickness from the model CRUST 1.0 [Laske et al., 2013] offer additional constrains. The resulting lithospheric density models are tested against gravity (GOCE) data. Various crustal and mantle density models have been tested in order to ascertain the effects that uncertainties in the estimate of those features have on the

Atmospheric models rely on high-accuracy, high-resolution initial radiometric and surface conditions for better short-term meteorological forecasts, as well as improved evaluation of global climate models. Continuous remote sensing of the Earth's energy budget, as conducted by the Clouds and Earth's Radiant Energy System (CERES) project, allows for near-realtime evaluation of cloud and surface radiation properties. It is unfortunately common for there to be bias between atmospheric/surface radiation models and Earth-observations. For example, satellite-observed surface skin temperature (Ts), an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface, can be biased due to atmospheric adjustment assumptions and anisotropy effects. Similarly, models are potentially biased by errors in initial conditions and regional forcing assumptions, which can be mitigated through assimilation with true measurements. As such, when frequent, broad-coverage, and accurate retrievals of satellite Ts are available, important insights into model estimates of Ts can be gained. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared method to produce anisotropy-corrected Ts over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) satellite imagers. Regional and diurnal changes in model landsurface temperature (LST) performance can be assessed owing to the somewhat continuous measurements of the LST offered by GEO satellites - measurements which are accurate to within 0.2 K. A seasonal, hourly comparison of satellite-observed LST with the NASA Goddard Earth Observing System Version 5 (GEOS-5) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA) LST estimates is conducted to reveal regional and diurnal biases. This assessment is an important first step for evaluating the effectiveness of Ts assimilation, as well for determining the

In global warming scenarios, global landsurface temperatures () warm with greater amplitude than sea surface temperatures (SSTs), leading to a land/sea warming contrast even in equilibrium. Similarly, the interannual variability of is larger than the covariant interannual SST variability, leading to a land/sea contrast in natural variability. This work investigates the land/sea contrast in natural variability based on global observations, coupled general circulation model simulations and idealised atmospheric general circulation model simulations with different SST forcings. The land/sea temperature contrast in interannual variability is found to exist in observations and models to a varying extent in global, tropical and extra-tropical bands. There is agreement between models and observations in the tropics but not the extra-tropics. Causality in the land-sea relationship is explored with modelling experiments forced with prescribed SSTs, where an amplification of the imposed SST variability is seen over land. The amplification of to tropical SST anomalies is due to the enhanced upper level atmospheric warming that corresponds with tropical moist convection over oceans leading to upper level temperature variations that are larger in amplitude than the source SST anomalies. This mechanism is similar to that proposed for explaining the equilibrium global warming land/sea warming contrast. The link of the to the dominant mode of tropical and global interannual climate variability, the El Niño Southern Oscillation (ENSO), is found to be an indirect and delayed connection. ENSO SST variability affects the oceans outside the tropical Pacific, which in turn leads to a further, amplified and delayed response of.

AGU 2015 Fall Meeting Session ID#: 7598 Remote Sensing Applications for Water Resources Management LandSurface Modeling Applications for Famine Early Warning James Verdin, USGS EROS Christa Peters-Lidard, NASA GSFC Amy McNally, NASA GSFC, UMD/ESSIC Kristi Arsenault, NASA GSFC, SAIC Shugong Wang, NASA GSFC, SAIC Sujay Kumar, NASA GSFC, SAIC Shrad Shukla, UCSB Chris Funk, USGS EROS Greg Fall, NOAA Logan Karsten, NOAA, UCAR Famine early warning has traditionally required close monitoring of agro-climatological conditions, putting them in historical context, and projecting them forward to anticipate end-of-season outcomes. In recent years, it has become necessary to factor in the effects of a changing climate as well. There has also been a growing appreciation of the linkage between food security and water availability. In 2009, Famine Early Warning Systems Network (FEWS NET) science partners began developing landsurface modeling (LSM) applications to address these needs. With support from the NASA Applied Sciences Program, an instance of the Land Information System (LIS) was developed to specifically support FEWS NET. A simple crop water balance model (GeoWRSI) traditionally used by FEWS NET took its place alongside the Noah landsurface model and the latest version of the Variable Infiltration Capacity (VIC) model, and LIS data readers were developed for FEWS NET precipitation forcings (NOAA's RFE and USGS/UCSB's CHIRPS). The resulting system was successfully used to monitor and project soil moisture conditions in the Horn of Africa, foretelling poor crop outcomes in the OND 2013 and MAM 2014 seasons. In parallel, NOAA created another instance of LIS to monitor snow water resources in Afghanistan, which are an early indicator of water availability for irrigation and crop production. These successes have been followed by investment in LSM implementations to track and project water availability in Sub-Saharan Africa and Yemen, work that is now underway. Adoption of

NASA has begun a process to identify and discuss candidate locations where humans could land, live and work on the Martian surface. These locations are referred to as Exploration Zones (EZs). Given current mission concepts, an EZ is a collection of Regions of Interest (ROIs) that are located within approximately 100 kilometers of a centralized landing site. ROIs are areas that are relevant for scientific investigation and/or development/maturation of capabilities and resources necessary for a sustainable human presence. The EZ also contains a landing site and a habitation site that will be used by multiple human crews during missions to explore and utilize the ROIs within the EZ. These candidate EZs will be used by NASA as part of a multi-year process of determining where and how humans could explore Mars. In the near term this process includes: (a) identifying locations that would maximize the potential science return from future human exploration missions, (b) identifying locations with the potential for resources required to support humans, (c) developing concepts and engineering systems needed by future human crews to conduct operations within an EZ, and (d) identifying key characteristics of the proposed candidate EZs that cannot be evaluated using existing data sets, thus helping to define precursor measurements needed in advance of human missions. Existing and future robotic spacecraft will be tasked to gather data from specific Mars surface sites within the representative EZs to support these NASA activities. The proposed paper will describe NASA's initial steps for identifying and evaluating candidate EZs and ROIs. This includes plans for the "First Landing Site/Exploration Zone Workshop for Human Missions to the Surface of Mars" to be held in October 2015 at which proposals for EZs and ROIs will be presented and discussed. It will also include a discussion of how these considerations are (or will be) taken into account as future robotic Mars missions are

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earthsurface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earthsurface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular

Shrubs and trees are generally considered to protect hillslopes from erosion. As a consequence, shrub encroachment on mountain pastures after abandoning grazing is not considered a threat to soils. However, the abandonment of mown or grazed grasslands causes a shift in vegetation composition and thus a change in landscape ecology and geomorphology. On many alpine slopes, current changes in land use and vegetation cover are accompanied by climate change, potentially generating a new geomorphic regime. Most of the debate focuses on the effect of land abandonment on water erosion rates. Generally, an established perennial vegetation cover improves the mechanical anchoring of the soil and the regulation of the soil water budget, including runoff generation and erosion. However, changing vegetation composition affects many other above- and below-ground properties like root density, -diversity and -geometry, soil structure, pore volume and acidity. Each combination of these properties can lead to a distinct scenario of dominating surface processes, often not reflected by common erosion risk assessment procedures. The study of soil properties along a chronosequence of green alder (alnusviridis) encroachment on the Unteralptal in central Switzerland reveals that shrub encroachment changes soil and vegetation properties towards an increase of resistance to run-off related erosion processes, but a decrease of slope stability against shallow landslides. The latter are a particular threat because of the currently increasing frequency of slide-triggering high magnitude rainfalls. The potential change of process domain on alpine pastures highlights the need for a careful use of erosion models when assessing future land use and climate scenarios. In mountains, but also other intensively managed agricultural landscapes, risk assessment without the appropriate reflection on the shifting relevance of surface processes carries the risk of missing future threats to environmental

Landsurface air temperature (LSAT) has been a widely used metric to study climate change. Weather observations of LSAT are the fundamental data for climate change studies and provide key evidence of global warming. However, there are very few meteorological observations over deserts due to their uninhabitable environment. This study fills this gap and provides independent evidence using satellite-derived landsurface temperatures (LSTs), benefiting from their global coverage. The frequency of clear sky from MODerate Resolution Imaging Spectroradiometer (MODIS) LST data over global deserts was found to be greater than 94% for the 2002-2015 period. Our results show that MODIS LST has a bias of 1.36°C compared to ground-based observations collected at 31 U.S. Climate Reference Network (USCRN) stations, with a standard deviation of 1.83°C. After bias correction, MODIS LST was used to evaluate existing reanalyses, including ERA-Interim, Japanese 55-year Reanalysis (JRA-55), Modern-Era Retrospective Analysis for Research and Applications (MERRA), MERRA-land, National Centers for Environmental Prediction (NCEP)-R1, and NCEP-R2. The reanalyses accurately reproduce the seasonal cycle and interannual variability of the LSTs, but their multiyear means and trends of LSTs exhibit large uncertainties. The multiyear averaged LST over global deserts is 23.5°C from MODIS and varies from 20.8°C to 24.5°C in different reanalyses. The MODIS LST over global deserts increased by 0.25°C/decade from 2002 to 2015, whereas the reanalyses estimated a trend varying from -0.14 to 0.10°C/decade. The underestimation of the LST trend by the reanalyses occurs for approximately 70% of the global deserts, likely due to the imperfect performance of the reanalyses in reproducing natural climate variability.

This study proposed a new approach of downscaling ETWATCH 1km actual evapotranspiration (ET) product to a spatial resolution of 30m using landsurface resistance that simulated mainly from monthly Landsat8 data and Jarvis method, which combined the benefits of both high temporal resolution of ETWATCH product and fine spatial resolution of Landsat8. The driving factor, surface resistance (Rs), was chosen for the reason that could reflect the transfer ability of vapor flow over canopy. Combined resistance Rs both upon canopy conditions, atmospheric factors and available water content of soil, which remains stable inside one ETWATCH pixel (1km). In this research, we used ETWATCH 1km ten-day actual ET product from April to October in a total of twenty-one images and monthly 30 meters cloud-free NDVI of 2013 (two images from HJ as a substitute due to cloud contamination) combined meteorological indicators for downscaling. A good agreement and correlation were obtained between the downscaled data and three flux sites observation in the middle reach of Heihe basin. The downscaling results show good consistency with the original ETWATCH 1km data both temporal and spatial scale over different land cover types with R2 ranged from 0.8 to 0.98. Besides, downscaled result captured the progression of vegetation transpiration well. This study proved the practicability of new downscaling method in the water resource management.

During the summer of 2016, a series of drop tests were conducted on two passive earth entry vehicle (EEV) test articles at the Utah Test and Training Range (UTTR). The tests were conducted to evaluate the structural integrity of a realistic EEV vehicle under anticipated landing loads. The test vehicles were lifted to an altitude of approximately 400m via a helicopter and released via release hook into a predesignated 61 m landing zone. Onboard accelerometers were capable of measuring vehicle free flight and impact loads. High-speed cameras on the ground tracked the free-falling vehicles and data was used to calculate critical impact parameters during the final seconds of flight. Additional sets of high definition and ultra-high definition cameras were able to supplement the high-speed data by capturing the release and free flight of the test articles. Three tests were successfully completed and showed that the passive vehicle design was able to withstand the impact loads from nominal and off-nominal impacts at landing velocities of approximately 29 m/s. Two out of three test resulted in off-nominal impacts due to a combination of high winds at altitude and the method used to suspend the vehicle from the helicopter. Both the video and acceleration data captured is examined and discussed. Finally, recommendations for improved release and instrumentation methods are presented.

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and landsurface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of landsurface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) landsurface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah landsurface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the landsurface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Changing vegetation cover not only affects the atmospheric concentration of greenhouse gases but also alters the radiative and non-radiative properties of the surface. The result of competing biophysical processes on Earth's surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and on the background climate. To date these effects are not accounted for in land-based climate policies because of the complexity of the phenomena, contrasting model predictions and the lack of global data-driven assessments. To overcome the limitations of available observation-based diagnostics and of the on-going model inter-comparison, here we present a new benchmarking dataset derived from satellite remote sensing. This global dataset provides the potential changes induced by multiple vegetation transitions on the single terms of the surface energy balance. We used this dataset for two major goals: 1) Quantify the impact of actual vegetation changes that occurred during the decade 2000-2010, showing the overwhelming role of tropical deforestation in warming the surface by reducing evapotranspiration despite the concurrent brightening of the Earth. 2) Benchmark a series of ESMs against data-driven metrics of the land cover change impacts on the various terms of the surface energy budget and on the surface temperature. We anticipate that the dataset could be also used to evaluate future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

We have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical regression method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of band-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NE(Delta)T) and calibration accuracy specifications of the MODIS instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 micrometer IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K. Several issues related to the day/night LST algorithm (uncertainties in the day/night registration and in surface emissivity changes caused by dew occurrence, and the cloud cover) have been investigated. The LST algorithms have been validated with MODIS Airborne Simulator (MAS) dada and ground-based measurement data in two field campaigns conducted in Railroad Valley playa, NV in 1995 and 1996. The MODIS LST version 1 software has been delivered.

In the domain of predicting landsurface fluxes, models are used to bring data from large observation networks and satellite remote sensing together to make predictions about present and future states of the Earth. Characterizing the uncertainty about such predictions is a complex process and one that is not yet fully understood. Uncertainty exists about initialization, measurement and interpolation of input variables; model parameters; model structure; and mixed spatial and temporal supports. Multiple models or structures often exist to describe the same processes. Uncertainty about structure is currently addressed by running an ensemble of different models and examining the distribution of model outputs. To illustrate structural uncertainty, a multi-model ensemble experiment we have been conducting using the Terrestrial Observation and Prediction System (TOPS) will be discussed. TOPS uses public versions of process-based ecosystem models that use satellite-derived inputs along with surface climate data and landsurface characterization to produce predictions of ecosystem fluxes including gross and net primary production and net ecosystem exchange. Using the TOPS framework, we have explored the uncertainty arising from the application of models with different assumptions, structures, parameters, and variable definitions. With a small number of models, this only begins to capture the range of possible spatial fields of ecosystem fluxes. Few attempts have been made to systematically address the components of uncertainty in such a framework. We discuss the characterization of uncertainty for this approach including both quantifiable and poorly known aspects.

A historical climatology of continuous satellite derived global landsurface soil moisture is being developed. The data set consists of surface soil moisture retrievals from observations of both historical and currently active satellite microwave sensors, including Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and AQUA AMSR-E. The data sets span the period from November 1978 through the end of 2006. The soil moisture retrievals are made with the Land Parameter Retrieval Model, a physically-based model which was developed jointly by researchers from the above institutions. These data are significant in that they are the longest continuous data record of observational surface soil moisture at a global scale. Furthermore, while previous reports have intimated that higher frequency sensors such as on SSM/I are unable to provide meaningful information on soil moisture, our results indicate that these sensors do provide highly useful soil moisture data over significant parts of the globe, and especially in critical areas located within the Earth's many arid and semi-arid regions.

Soil moisture availability exerts a strong control over land evaporation in many regions. However, global climate models (GCMs) disagree on when and where evaporation is limited by soil moisture. Evaluation of the relevant modelled processes has suffered from a lack of reliable, global observations of land evaporation at the GCM grid box scale. Satellite observations of landsurface temperature (LST) offer spatially extensive but indirect information about the surface energy partition and, under certain conditions, about soil moisture availability on evaporation. Specifically, as soil moisture decreases during rain-free dry spells, evaporation may become limited leading to increases in LST and sensible heat flux. We use MODIS Terra and Aqua observations of LST at 1 km from 2000 to 2012 to assess changes in the surface energy partition during dry spells lasting 10 days or longer. The clear-sky LST data are aggregated to a global 0.5° grid before being composited as a function dry spell day across many events in a particular region and season. These composites are then used to calculate a Relative Warming Rate (RWR) between the landsurface and near-surface air. This RWR can diagnose the typical strength of short term changes in surface heat fluxes and, by extension, changes in soil moisture limitation on evaporation. Offline landsurface model (LSM) simulations offer a relatively inexpensive way to evaluate the surface processes of GCMs. They have the benefits that multiple models, and versions of models, can be compared on a common grid and using unbiased forcing. Here, we use the RWR diagnostic to assess global, offline simulations of several LSMs (e.g., JULES and JSBACH) driven by the WATCH Forcing Data-ERA Interim. Both the observed RWR and the LSMs use the same 0.5° grid, which allows the observed clear-sky sampling inherent in the underlying MODIS LST to be applied to the model outputs directly. This approach avoids some of the difficulties in analysing free

Monsoon depressions (MDs) constitute a large fraction of the total rainfall during the Indian summer monsoon season. In this study, the impact of high-resolution land state is addressed by assessing the evolution of inland moving depressions formed over the Bay of Bengal using a mesoscale modeling system. Improved land state is generated using High Resolution Land Data Assimilation System employing Noah-MP land-surface model. Verification of soil moisture using Soil Moisture and Ocean Salinity (SMOS) and soil temperature using tower observations demonstrate promising results. Incorporating high-resolution land state yielded least root mean squared errors with higher correlation coefficient in the surface and mid tropospheric parameters. Rainfall forecasts reveal that simulations are spatially and quantitatively in accordance with observations and provide better skill scores. The improved landsurface characteristics have brought about the realistic evolution of surface, mid-tropospheric parameters, vorticity and moist static energy that facilitates the accurate MDs dynamics in the model. Composite moisture budget analysis reveals that the surface evaporation is negligible compared to moisture flux convergence of water vapor, which supplies moisture into the MDs over land. The temporal relationship between rainfall and moisture convergence show high correlation, suggesting a realistic representation of land state help restructure the moisture inflow into the system through rainfall-moisture convergence feedback.

Morphological evidence for ancient channelized flows (fluvial and fluvial-like landforms) exists on the surfaces of all of the inner planets and on some of the satellites of the Solar System. In some cases, the relevant fluid flows are related to a planetary evolution that involves the global cycling of a volatile component (water for Earth and Mars; methane for Saturn’s moon Titan). In other cases, as on Mercury, Venus, Earth’s moon, and Jupiter’s moon Io, the flows were of highly fluid lava. The discovery, in 1972, of what are now known to be fluvial channels and valleys on Mars sparked a major controversy over the role of water in shaping the surface of that planet. The recognition of the fluvial character of these features has opened unresolved fundamental questions about the geological history of water on Mars, including the presence of an ancient ocean and the operation of a hydrological cycle during the earliest phases of planetary history. Other fundamental questions posed by fluvial and fluvial-like features on planetary bodies include the possible erosive action of large-scale outpourings of very fluid lavas, such as those that may have produced the remarkable canali forms on Venus; the ability of exotic fluids, such as methane, to create fluvial-like landforms, as observed on Saturn’s moon, Titan; and the nature of sedimentation and erosion under different conditions of planetary surface gravity. Planetary fluvial geomorphology also illustrates fundamental epistemological and methodological issues, including the role of analogy in geomorphological/geological inquiry. PMID:29176917

The Community Surface Dynamics Modeling System (CSDMS) represents a diverse group of >1300 scientists who develop and apply numerical models to better understand the Earth's surface. CSDMS has a mandate to make the public more aware of model capabilities and therefore started sharing state-of-the-art surface process modeling results with large audiences. One platform to reach audiences outside the science community is through museum displays on 'Science on a Sphere' (SOS). Developed by NOAA, SOS is a giant globe, linked with computers and multiple projectors and can display data and animations on a sphere. CSDMS has developed and contributed model simulation datasets for the SOS system since 2014, including hydrological processes, coastal processes, and human interactions with the environment. Model simulations of a hydrological and sediment transport model (WBM-SED) illustrate global river discharge patterns. WAVEWATCH III simulations have been specifically processed to show the impacts of hurricanes on ocean waves, with focus on hurricane Katrina and super storm Sandy. A large world dataset of dams built over the last two centuries gives an impression of the profound influence of humans on water management. Given the exposure of SOS, CSDMS aims to contribute at least 2 model datasets a year, and will soon provide displays of global river sediment fluxes and changes of the sea ice free season along the Arctic coast. Over 100 facilities worldwide show these numerical model displays to an estimated 33 million people every year. Datasets storyboards, and teacher follow-up materials associated with the simulations, are developed to address common core science K-12 standards. CSDMS dataset documentation aims to make people aware of the fact that they look at numerical model results, that underlying models have inherent assumptions and simplifications, and that limitations are known. CSDMS contributions aim to familiarize large audiences with the use of numerical

Community landsurface models such as JULES are increasingly used for hydrological assessment because of their state-of-the-art representation of land-surface processes. However, a major weakness of JULES and other landsurface models is the limited number of landsurface parameterizations that is available. Therefore, this study explores the use of data from a network of catchments under homogeneous land-use to generate parameter "libraries" to extent the landsurface parameterizations of JULES. The network (called iMHEA) is part of a grassroots initiative to characterise the hydrological response of different Andean ecosystems, and collects data on streamflow, precipitation, and several weather variables at a high temporal resolution. The tropical Andes are a useful case study because of the complexity of meteorological and geographical conditions combined with extremely heterogeneous land-use that result in a wide range of hydrological responses. We then calibrated JULES for each land-use represented in the iMHEA dataset. For the individual land-use types, the results show improved simulations of streamflow when using the calibrated parameters with respect to default values. In particular, the partitioning between surface and subsurface flows can be improved. But also, on a regional scale, hydrological modelling was greatly benefitted from constraining parameters using such distributed citizen-science generated streamflow data. This study demonstrates the modelling and prediction on regional hydrology by integrating citizen science and landsurface model. In the context of hydrological study, the limitation of data scarcity could be solved indeed by using this framework. Improved predictions of such impacts could be leveraged by catchment managers to guide watershed interventions, to evaluate their effectiveness, and to minimize risks.

Exoplanet discovery has made remarkable progress, with the first rocky planets having been detected in the central star's liquid water habitable zone. The remote sensing techniques used to characterize such planets for potential habitability and life rely solely on our understanding of life on Earth. The vegetation red edge from terrestrial land plants is often used as a direct signature of life, but it occupies only a small niche in the environmental parameter space that binds life on present-day Earth and has been widespread for only about 460 My. To more fully exploit the diversity of the one example of life known, we measured the spectral characteristics of 137 microorganisms containing a range of pigments, including ones isolated from Earth's most extreme environments. Our database covers the visible and near-infrared to the short-wavelength infrared (0.35-2.5 µm) portions of the electromagnetic spectrum and is made freely available from biosignatures.astro.cornell.edu. Our results show how the reflectance properties are dominated by the absorption of light by pigments in the visible portion and by strong absorptions by the cellular water of hydration in the infrared (up to 2.5 µm) portion of the spectrum. Our spectral library provides a broader and more realistic guide based on Earth life for the search for surface features of extraterrestrial life. The library, when used as inputs for modeling disk-integrated spectra of exoplanets, in preparation for the next generation of space- and ground-based instruments, will increase the chances of detecting life.

Assessing and quantifying the uncertainties in projected future changes of energy and water budgets over landsurface are important steps toward improving our confidence in climate change projections. In our study, the contribution of landsurface models to the inter-GCM variation of projected future changes in landsurface energy and water fluxes are assessed based on output from 19 global climate models (GCMs) and offline Community Land Model version 4 (CLM4) simulations driven by meteorological forcing from the 19 GCMs. Similar offline simulations using CLM4 with its dynamic vegetation submodel are also conducted to investigate how dynamic vegetation feedback, amore » process that is being added to more earth system models, may amplify or moderate the intermodel variations of projected future changes. Projected changes are quantified as the difference between the 2081–2100 period from the Representative Concentration Pathway 8.5 (RCP8.5) future experiment and the 1981–2000 period from the historical simulation. Under RCP8.5, projected changes in surface water and heat fluxes show a high degree of model dependency across the globe. Although precipitation is very likely to increase in the high latitudes of the Northern Hemisphere, a high degree of model-related uncertainty exists for evapotranspiration, soil water content, and surface runoff, suggesting discrepancy among landsurface models (LSMs) in simulating the surface hydrological processes and snow-related processes. Large model-related uncertainties for the surface water budget also exist in the Tropics including southeastern South America and Central Africa. Moreover, these uncertainties would be reduced in the hypothetical scenario of a single near-perfect landsurface model being used across all GCMs, suggesting the potential to reduce uncertainties through the use of more consistent approaches toward landsurface model development. Under such a scenario, the most significant reduction is likely to

Assessing and quantifying the uncertainties in projected future changes of energy and water budgets over landsurface are important steps toward improving our confidence in climate change projections. In our study, the contribution of landsurface models to the inter-GCM variation of projected future changes in landsurface energy and water fluxes are assessed based on output from 19 global climate models (GCMs) and offline Community Land Model version 4 (CLM4) simulations driven by meteorological forcing from the 19 GCMs. Similar offline simulations using CLM4 with its dynamic vegetation submodel are also conducted to investigate how dynamic vegetation feedback, amore » process that is being added to more earth system models, may amplify or moderate the intermodel variations of projected future changes. Projected changes are quantified as the difference between the 2081–2100 period from the Representative Concentration Pathway 8.5 (RCP8.5) future experiment and the 1981–2000 period from the historical simulation. Under RCP8.5, projected changes in surface water and heat fluxes show a high degree of model dependency across the globe. Although precipitation is very likely to increase in the high latitudes of the Northern Hemisphere, a high degree of model-related uncertainty exists for evapotranspiration, soil water content, and surface runoff, suggesting discrepancy among landsurface models (LSMs) in simulating the surface hydrological processes and snow-related processes. Large model-related uncertainties for the surface water budget also exist in the Tropics including southeastern South America and Central Africa. Moreover, these uncertainties would be reduced in the hypothetical scenario of a single near-perfect landsurface model being used across all GCMs, suggesting the potential to reduce uncertainties through the use of more consistent approaches toward landsurface model development. Under such a scenario, the most significant reduction is likely to

Satellite remote sensing, including the thermal infrared (TIR) and passive microwave (MW), provides the possibility to observe LST at large scales. For better modeling the landsurface processes with high temporal resolutions, all-weather LST from satellite data is desirable. However, estimation of all-weather LST faces great challenges. On the one hand, TIR remote sensing is limited to clear-sky situations; this drawback reduces its usefulness under cloudy conditions considerably, especially in regions with frequent and/or permanent clouds. On the other hand, MW remote sensing suffers from much greater thermal sampling depth (TSD) and coarser spatial resolution than TIR; thus, MW LST is generally lower than TIR LST, especially at daytime. Two case studies addressing the challenges mentioned previously are presented here. The first study is for the development of a novel thermal sampling depth correction method (TSDC) to estimate the MW LST over barren land; this second study is for the development of a feasible method to merge the TIR and MW LSTs by addressing the coarse resolution of the latter one. In the first study, the core of the TSDC method is a new formulation of the passive microwave radiation balance equation, which allows linking bulk MW radiation to the soil temperature at a specific depth, i.e. the representative temperature: this temperature is then converted to LST through an adapted soil heat conduction equation. The TSDC method is applied to the 6.9 GHz channel in vertical polarization of AMSR-E. Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation is based on in-situ LSTs measured at the Gobabeb site in western Namibia. The results demonstrate the high accuracy of the TSDC method: it yields a root-mean squared error (RMSE) of 2 K and ignorable systematic error over barren land. In the second study, the method consists of two core processes: (1) estimation of MW LST from MW brightness temperature and (2

Thermal stressing induces microcrack growth in rock in part due to thermal expansion mismatch between different minerals, mineral phases, or crystalline axes and/or thermal gradients in the entire rock mass. This knowledge is largely derived from experimental studies of thermal microcracking, typically under conditions of very high temperatures (hundreds of °C). Thermal stressing at lower temperatures has received significantly less attention despite the fact that it may play an important role in rock breakdown at and near Earth's surface (Aldred et al., 2015; Collins and Stock, 2016). In particular, Eppes et al. (2016) attribute recorded Acoustic Emissions (AE) from a highly instrumented granite boulder sitting on the ground in natural conditions to subcritical crack growth driven by thermal stresses arising from a combination of solar- and weather-induced temperature changes; however the maximum temperature the boulder experienced was just 65 °C. In order to better understand these results without complicating factors of a natural environment, we conducted controlled laboratory experiments on cylindrical samples (40 mm length and 20 mm diameter) cored from the same granite as the Eppes et al. (2016) experiment, subjecting them to temperature fluctuations that reproduced the field measurements. We used a novel experimental configuration whereby two high temperature piezo-transducers are each in contact with an opposing face of the sample. The servo-controlled uniaxial press compensates for the thermal expansion and contraction of the pistons and the sample, keeping the coupling between the transducers and the sample, and the axial force acting on the sample, constant throughout. The system records AE, as well as P-wave velocity, both independent proxies for microfracture, as well as strain and temperature. Preliminary tests, heating and cooling granite at a rate of 1 °C/min, show that a large amount of AE occurs at temperatures as low as 100 °C. Ultimately, by

Rare-earth (RE) oxide surfaces are of significant importance for catalysis and were recently reported to possess intrinsic hydrophobicity. The surface chemistry of these oxides in the low temperature regime, however, remains to a large extent unexplored. The reactions occurring at RE surfaces at room temperature (RT) in real air environment, in particular, in presence of polycyclic aromatic hydrocarbons (PAHs), were not addressed until now. Discovering these reactions would shed light onto intermediate steps occurring in automotive exhaust catalysts before reaching the final high operational temperature and full conversion of organics. Here we first address physical properties of the RE oxide, nitride and fluoride surfaces modified by exposure to ambient air and then we report a room temperature reaction between PAH and RE oxide surfaces, exemplified by tetracene (C18H12) on a Gd2O3. Our study evidences a novel effect - oxidation of higher hydrocarbons at significantly lower temperatures (~300 K) than previously reported (>500 K). The evolution of the surface chemical composition of RE compounds in ambient air is investigated and correlated with the surface wetting. Our surprising results reveal the complex behavior of RE surfaces and motivate follow-up studies of reactions between PAH and catalytic surfaces at the single molecule level.

The EC-Earthearth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning retrospective predictions at the decadal (5-years), seasonal and sub-seasonal time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and sub-seasonal time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional landsurface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste

The EC-Earthearth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional landsurface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over

The EC-Earthearth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional landsurface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel

Dry deposition is a key component of surface-atmosphere exchange of compounds, acting as a sink for several chemical species. Meteorological factors, chemical properties of the trace gas considered and landsurface properties are strong drivers of dry deposition efficiency and variability. Under both climatic and anthropogenic pressure, the vegetation distribution over the Earth has been changing a lot over the past centuries and could be significantly altered in the future. In this study, we perform a modeling investigation of the potential impact of land-cover changes between the present day (2006) and the future (2050) on dry deposition velocities at the surface, with special interest for ozone (O3) and nitric acid (HNO3), two compounds which are characterized by very different physicochemical properties. The 3-D chemistry-transport model LMDz-INCA is used, considering changes in vegetation distribution based on the three future projections, RCPs 2.6, 4.5 and 8.5, and present-day (2007) meteorology. The 2050 RCP 8.5 vegetation distribution leads to a rise of up to 7 % (+0.02 cm s-1) in the surface deposition velocity calculated for ozone (Vd,O3) and a decrease of -0.06 cm s-1 in the surface deposition velocity calculated for nitric acid (Vd,HNO3) relative to the present-day values in tropical Africa and up to +18 and -15 %, respectively, in Australia. When taking into account the RCP 4.5 scenario, which shows dramatic land-cover change in Eurasia, Vd,HNO3 increases by up to 20 % (annual-mean value) and reduces Vd,O3 by the same magnitude in this region. When analyzing the impact of surface dry deposition change on atmospheric chemical composition, our model calculates that the effect is lower than 1 ppb on annual-mean surface ozone concentration for both the RCP 8.5 and RCP 2.6 scenarios. The impact on HNO3 surface concentrations is more disparate between the two scenarios regarding the spatial repartition of effects. In the case of the RCP 4.5 scenario, a

An evaluation of the clear-sky longwave irradiance at the earth's surface (LI) simulated in climate models and in satellite-based global datasets is presented. Algorithm-based estimates of LI, derived from global observations of column water vapor and surface (or screen air) temperature, serve as proxy `observations.' All datasets capture the broad zonal variation and seasonal behavior in LI, mainly because the behavior in column water vapor and temperature is reproduced well. Over oceans, the dependence of annual and monthly mean irradiance upon sea surface temperature (SST) closely resembles the observed behavior of column water with SST. In particular, the observed hemispheric difference in the summer minus winter column water dependence on SST is found in all models, though with varying seasonal amplitudes. The analogous behavior in the summer minus winter LI is seen in all datasets. Over land, all models have a more highly scattered dependence of LI upon surface temperature compared with the situation over the oceans. This is related to a much weaker dependence of model column water on the screen-air temperature at both monthly and annual timescales, as observed. The ability of climate models to simulate realistic LI fields depends as much on the quality of model water vapor and temperature fields as on the quality of the longwave radiation codes. In a comparison of models with observations, root-mean-square gridpoint differences in mean monthly column water and temperature are 4-6 mm (5-8 mm) and 0.5-2 K (3-4 K), respectively, over large regions of ocean (land), consistent with the intermodel differences in LI of 5-13 W m2 (15-28 W m2).

The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor landsurface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of-atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked ("remembered") over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance

On Dec 18, 1999, NASA launched the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument on the Earth Observing System (EOS) Terra mission, in a spectacular launch. The mission will provide morning (10:30 AM) global observations of aerosol and other related parameters. It will be followed a year later by a MODIS instrument on EOS Aqua for afternoon observations (1:30 PM). MODIS will measure aerosol over land and ocean with its eight 500 m and 250 m channels in the solar spectrum (0-41 to 2.2 micrometers). Over the land MODIS will measure the total column aerosol loading, and distinguish between submicron pollution particles and large soil particles. Standard daily products of resolution of ten kilometers and global mapped eight day and monthly products on a 1x1 degree global scale will be produced routinely and make available for no or small reproduction charge to the international community. Though the aerosol products will not be available everywhere over the land, it is expected that they will be useful for assessments of the presence, sources and transport of urban pollution, biomass burning aerosol, and desert dust. Other measurements from MODIS will supplement the aerosol information, e.g., land use change, urbanization, presence and magnitude of biomass burning fires, and effect of aerosol on cloud microphysics. Other instruments on Terra, e.g. Multi-angle Imaging SpectroRadiometer (MISR) and the Clouds and the Earth's Radiant Energy System (CERES), will also measure aerosol, its properties and radiative forcing in tandem with the MODIS measurements. During the Aqua period, there are plans to launch in 2003 the Pathfinder Instruments for Cloud and Aerosol Spaceborne Observations (PICASSO) mission for global measurements of the aerosol vertical structure, and the PARASOL mission for aerosol characterization. Aqua-MODIS, PICASSO and PARASOL will fly in formation for detailed simultaneous characterization of the aerosol three-dimensional field, which

This report summarizes the accomplishments made by the MODIS LST (Land-Surface Temperature) group at University of California, Santa Barbara, under NASA Contract. Version 1 of the MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (ATBD) was reviewed in June 1994, version 2 reviewed in November 1994, version 3.1 in August 1996, and version 3.3 updated in April 1999. Based on the ATBD, two LST algorithms were developed, one is the generalized split-window algorithm and another is the physics-based day/night LST algorithm. These two LST algorithms were implemented into the production generation executive code (PGE 16) for the daily standard MODIS LST products at level-2 (MODII-L2) and level-3 (MODIIA1 at 1 km resolution and MODIIB1 at 5km resolution). PGE codes for 8-day 1 km LST product (MODIIA2) and the daily, 8-day and monthly LST products at 0.05 degree latitude/longitude climate model grids (CMG) were also delivered. Four to six field campaigns were conducted each year since 2000 to validate the daily LST products generated by PGE16 and the calibration accuracies of the MODIS TIR bands used for the LST/emissivity retrieval from versions 2-4 of Terra MODIS data and versions 3-4 of Aqua MODIS data. Validation results from temperature-based and radiance-based methods indicate that the MODIS LST accuracy is better than 1 C in most clear-sky cases in the range from -10 to 58 C. One of the major lessons learn from multi- year temporal analysis of the consistent V4 daily Terra MODIS LST products in 2000-2003 over some selected target areas including lakes, snow/ice fields, and semi-arid sites is that there are variable numbers of cloud-contaminated LSTs in the MODIS LST products depending on surface elevation, land cover types, and atmospheric conditions. A cloud-screen scheme with constraints on spatial and temporal variations in LSTs was developed to remove cloud-contaminated LSTs. The 5km LST product was indirectly validated through comparisons to

Inland surface water is essential to terrestrial ecosystems and human civilization. The distribution of surface water in space and its change over time are related to many agricultural, environmental and ecological issues, and are important factors that must be considered in human socioeconomic development. Accurate mapping of surface water is essential for both scientific research and policy-driven applications. Satellite-based remote sensing provides snapshots of Earth's surface and can be used as the main input for water mapping, especially in large areas. Global water areas have been mapped with coarse resolution remotely sensed data (e.g., the Moderate Resolution Imaging Spectroradiometer (MODIS)). However, most inland rivers and water bodies, as well as their changes, are too small to map at such coarse resolutions. Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) imagery has a 30m spatial resolution and provides decades of records (~40 years). Since 2008, the opening of the Landsat archive, coupled with relatively lower costs associated with computing and data storage, has made comprehensive study of the dynamic changes of surface water over large even global areas more feasible. Although Landsat images have been used for regional and even global water mapping, the method can hardly be automated due to the difficulties on distinguishing inland surface water with variant degrees of impurities and mixing of soil background with only Landsat data. The spectral similarities to other land cover types, e.g., shadow and glacier remnants, also cause misidentification. We have developed a probabilistic based automatic approach for mapping inland surface water bodies. Landsat surface reflectance in multiple bands, derived water indices, and data from other sources are integrated to maximize the ability of identifying water without human interference. The approach has been implemented with open-source libraries to facilitate processing large

Land Cover (LC) and its bio-geo-physical feedbacks are important for the understanding of climate and its vulnerability to changes on the surface of the Earth. Recently ESA has published a new LC map derived by combining remotely sensed surface reflectance and ground-truth observations. For each grid-box at 300m resolution, an estimate of confidence is provided. This LC data set can be used in climate modelling to derive landsurface boundary parameters for the respective LandSurface Model (LSM). However, the ESA LC classes are not directly suitable for LSMs, therefore they need to be converted into the model specific surface presentations. Due to different design and processes implemented in various climate models they might differ in the treatment of artificial, water bodies, ice, bare or vegetated surfaces. Nevertheless, usually vegetation distribution in models is presented by means of plant functional types (PFT), which is a classification system used to simplify vegetation representation and group different vegetation types according to their biophysical characteristics. The method of LC conversion into PFT is also called "cross-walking" (CW) procedure. The CW procedure is another source of uncertainty, since it depends on model design and processes implemented and resolved by LSMs. These two sources of uncertainty, (i) due to surface reflectance conversion into LC classes, (ii) due to CW procedure, have been studied by Hartley et al (2016) to investigate their impact on LSM state variables (albedo, evapotranspiration (ET) and primary productivity) by using three standalone LSMs. The present study is a follow up to that work and aims at quantifying the impact of these two uncertainties on climate simulations performed with the Max Planck Institute for Meteorology Earth System Model (MPI-ESM) using prescribed sea surface temperature and sea ice. The main focus is on the terrestrial water cycle, but the impacts on surface albedo, wind patterns, 2m temperatures

During atmospheric thermal inversions, dew and hoarfrost concentrate gamma emitting radionuclides of the short-lived (222)Rn progeny ((214)Pb and (214)Bi), causing an increase in the total natural gamma background from the ground. To highlight this phenomenon, a volcanic zone of high (222)Rn flux was studied during the winter season 2010-11. High-specific short-lived radon progeny activities up to 122 Bq g(-1) were detected in hydrometeors forming at the earth's surface (ESHs), corresponding to a mean increase of up to 17 % of the normal gamma background value. A theoretical model, depending on radon flux from soil and predicting the radon progeny concentrations in hydrometeors forming at the ESHs is presented. The comparison between model and field data shows a good correspondence. Around nuclear power plants or in nuclear facilities that use automatic NaI or CsI total gamma spectroscopy systems for monitoring radioactive contamination, hydrometeors forming at the ESHs in sites with a high radon flux could represent a relevant source of false alarms of radioactive contamination.

The NASA MEaSUReS program was put into place to produce long-term, well calibrated and validated data records for Earth Science research. As part of this program, we have developed three Earth System Data Records (ESDR) to measure LandSurface Temperature (LST) and emissivity: a high spatial resolution (1km) LST product using Low Earth Orbiting (LEO) satellites; a high temporal resolution (hourly over North America) LST product using Geostationary (GEO) satellites; and a Combined ASTER MODIS Emissivity for Land (CAMEL) ESDR. CAMEL was produced by merging two state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UWIREMIS), and the JPL ASTER Global Emissivity Dataset v4 (GEDv4). The CAMEL ESDR is currently available for download, and is being tested in sounder retrieval schemes (e.g. CrIS, IASI, AIRS) to reduce uncertainties in water vapor retrievals, and has already been implemented in the radiative transfer software RTTOV v12 for immediate use in numerical weather modeling and data assimilation systems. The LEO-LST product combines two existing MODIS products, using an uncertainty analysis approach to optimize accuracy over different landcover classes. Validation of these approaches for retrieving LST have shown that they are complementary, with the split-window approach (MxD11) being more stable over heavily vegetated regions and the physics-based approach (MxD21) demonstrating higher accuracy in semi-arid and arid regions where the largest variations in emissivity exist, both spatially and spectrally. The GEO LST-ESDR product uses CAMEL ESDR for improved temperature-emissivity separation, and the same atmospheric correction as the LEO LST product to ensure consistency across all three data records.

For the next round of CMIP6 climate simulations there are new historical and SSP - RCP land use and land cover change (LULCC) data sets that have been compiled through the Land Use Model Intercomparison Project (LUMIP). The new time series data include new functionality following lessons learned through CMIP5 project and include new developments in the Community Land Model (CLM5) that will be used in all the CESM2 simulations of CMIP6. These changes include representing explicit crop modeling and better forest representation through the extended to 12 land units of the Global Land Model (GLM). To include this new information in CESM2 and CLM5 simulations new transient landsurface data sets have been generated for the historical period 1850 - 2015 and for preliminary SSP - RCP paired future scenarios. The new data sets use updated MODIS Land Cover, Vegetation Continuous Fields, Leaf Area Index and Albedo to describe Primary and Secondary, Forested and Non Forested land units, as well as Rangelands and Pasture. Current day crop distributions are taken from the MIRCA2000 crop data set as done with the CLM 4.5 crop model and used to guide historical and future crop distributions. Preliminary "land only" simulations with CLM5 have been performed for the historical period and for the SSP1-RCP2.6 and SSP3-RCP7 land use and land cover change time series data. Equivalent no land use and land cover change simulations have been run for these periods under the same meteorological forcing data. The "land only" simulations use GSWP3 historical atmospheric forcing data from 1850 to 2010 and then time increasing RCP 8.5 atmospheric CO2 and climate anomalies on top of the current day GSWP3 atmospheric forcing data from 2011 to 2100. The offline simulations provide a basis to evaluate the surface climate, carbon cycle and crop production impacts of changing land use and land cover for each of these periods. To further evaluate the impacts of the new CLM5 model and the CMIP6 land

We made modifications to the linear kernel bidirectional reflectance distribution function (BRDF) models from Roujean et al. and Wanner et al. that extend the spectral range into the thermal infrared (TIR). With these TIR BRDF models and the IGBP land-cover product, we developed a classification-based emissivity database for the EOS/MODIS land-surface temperature (LST) algorithm and used it in version V2.0 of the MODIS LST code. Two V2.0 LST codes have been delivered to the MODIS SDST, one for the daily L2 and L3 LST products, and another for the 8-day 1km L3 LST product. New TIR thermometers (broadband radiometer with a filter in the 10-13 micron window) and an IR camera have been purchased in order to reduce the uncertainty in LST field measurements due to the temporal and spatial variations in LST. New improvements have been made to the existing TIR spectrometer in order to increase its accuracy to 0.2 C that will be required in the vicarious calibration of the MODIS TIR bands.

This paper addresses the problem of handoff in a land mobile satellite (LMS) system between adjacent satellites in a low earth orbit (LEO) constellation. In particular, emphasis is placed on the application of soft handoff in a direct sequence code division multiple access (DS-CDMA) LMS system. Soft handoff is explained in terms of terrestrial macroscopic diversity, in which signals transmitted via several independent fading paths are combined to enhance the link quality. This concept is then reconsidered in the context of a LEO LMS system. A two-state Markov channel model is used to simulate the effects of shadowing on the communications path from the mobile to each satellite during handoff. The results of the channel simulation form a platform for discussion regarding soft handoff, highlighting the potential merits of the scheme when applied in a LEO LMS environment.

Leafy spurge (Euphorbia esula L.) is an invasive exotic plant that can completely displace native plant communities. Automated techniques for monitoring the location and extent of leafy spurge, especially if available on a seasonal basis, could add greatly to the effectiveness of control measures. As part of a larger study including multiple sensors, this study examines the utility of mapping the location and extent of leafy spurge in Theodore Roosevelt National Park using Earth Observing-1 satellite Advanced Land Imager (ALI) scanner data. An unsupervised classification methodology was used producing accuracies in the range of 59% to 66%. Existing field studies, with their associated limitations, were used for identifying class membership and accuracy assessment. This sensor could be useful for broad landscape scale mapping of leafy spurge, from which control measures could be based.

The AIRS Science Team Version-6 data set is a valuable resource for meteorological studies. Quality Controlled earth's surface skin temperatures are produced on a 45 km x 45 km spatial scale under most cloud cover conditions. The same retrieval algorithm is used for all surface types under all conditions. This study used eleven years of AIRS monthly mean surface skin temperature and cloud cover products to show that landsurface skin temperatures have decreased significantly in some areas and increased significantly in other areas over the period September 2002 through August 2013. These changes occurred primarily at 1:30 PM but not at 1:30 AM. Cooling land areas contained corresponding increases in cloud cover over this time period, with the reverse being true for warming land areas. The cloud cover anomaly patterns for a given month are affected significantly by El Nino/La Nina activity, and anomalies in cloud cover are a driving force behind anomalies in landsurface skin temperature.

Drumlins are glacially derived landforms that are prominent in the landscape over much of southern New England. We carried out a comprehensive ground-based survey in a three-town study area in eastern Massachusetts with the goals of establishing the extent to drumlins have been altered and assessing the associated environmental consequences and probable driving factors. Results show that many drumlins have been significantly altered through levelling and truncation (creation of steep cut and fill slopes), with projects involving movement of 1−1.5×106 m3 of earth materials not now uncommon. Stormwater and wetlands infractions were documented at all the larger excavation sites and resulted in enforcement actions and fines in many cases; the broader environmental consequences of the loss/alteration of these forested uplands are harder to establish. The excavations are significant in terms of materials cycling: the movement of earth materials, when considered regionally, greatly exceeds natural denudation processes and is also greater than during other periods of high anthropogenic denudation. Our findings suggest that the region’s glacial landscapes are at risk given current development patterns. The accelerating rate of land-surface change is undoubtedly also generalizable to other fast-developing regions of the United States. The landform alterations documented are part of a changing pattern of land use and vegetation cover since the Colonial era and are linked to shortages of land for development, current development and building practices, and lack of explicit rationales for preservation of the region’s geoheritage. PMID:23056410

Following successful support of the Northern Eurasia Earth Sciences Partner Initiative (NEESPI) project with NASA satellite remote sensing data, from Spring 2009 the NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has been working on collecting more satellite and model data to support the Monsoon Asia Integrated Regional Study (MAIRS) project. The established data management and service infrastructure developed for NEESPI has been used and improved for MAIRS support.Data search, subsetting, and download functions are available through a single system. A customized Giovanni system has been created for MAIRS.The Web-based on line data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) allows scientists to explore, quickly analyze, and download data easily without learning the original data structure and format. Giovanni MAIRS includes satellite observations from multiple sensors and model output from the NASA Global Land Data Assimilation System (GLDAS), and from the NASA atmospheric reanalysis project, MERRA. Currently, we are working on processing and integrating higher resolution land data in to Giovanni, such as vegetation index, landsurface temperature, and active fire at 5km or 1km from the standard MODIS products. For data that are not archived at the GESDISC,a product metadata portal is under development to serve as a gateway for providing product level information and data access links, which include both satellite, model products and ground-based measurements information collected from MAIRS scientists.Due to the large overlap of geographic coverage and many similar scientific interests of NEESPI and MAIRS, these data and tools will serve both projects.

Stratospheric ozone levels are near their lowest point since measurements began, so current ultraviolet-B (UV-B) radiation levels are thought to be close to their maximum. Total stratospheric content of ozone-depleting substances is expected to reach a maximum before the year 2000. All other things being equal, the current ozone losses and related UV-B increases should be close to their maximum. Increases in surface erythemal (sunburning) UV radiation relative to the values in the 1970s are estimated to be: about 7% at Northern Hemisphere mid-latitudes in winter/spring; about 4% at Northern Hemisphere mid-latitudes in summer/fall; about 6% at Southern Hemisphere mid-latitudes on a year-round basis; about 130% in the Antarctic in spring; and about 22% in the Arctic in spring. Reductions in atmospheric ozone are expected to result in higher amounts of UV-B radiation reaching the Earth's surface. The expected correlation between increases in surface UV-B radiation and decreases in overhead ozone has been further demonstrated and quantified by ground-based instruments under a wide range of conditions. Improved measurements of UV-B radiation are now providing better geographical and temporal coverage. Surface UV-B radiation levels are highly variable because of cloud cover, and also because of local effects including pollutants and surface reflections. These factors usually decrease atmospheric transmission and therefore the surface irradiances at UV-B as well as other wavelengths. Occasional cloud-induced increases have also been reported. With a few exceptions, the direct detection of UV-B trends at low- and mid-latitudes remains problematic due to this high natural variability, the relatively small ozone changes, and the practical difficulties of maintaining long-term stability in networks of UV-measuring instruments. Few reliable UV-B radiation measurements are available from pre-ozone-depletion days. Satellite-based observations of atmospheric ozone and clouds are

The international land-cover community has been working with GEO since 2005 to build the foundations for land-cover observations as an integral part of a Global Earth Observation System of Systems (GEOSS). The Group on Earth Observation (GEO) has provided the platform to elevate the societal relevance of land cover monitoring and helped to link a diverse set of global, regional, and national activities. A dedicated 2007-2009 GEO work plan task has resulted in achievements on the strategic and implementation levels. Integrated Global Observations of the Land (IGOL), the land theme of the Integrated Global Observation Strategy (IGOS), has been approved and is now in the process of transition into GEO implementation. New global land-cover maps at moderate spatial resolutions (i.e., GLOBCOVER) are being produced using guidelines and standards of the international community. The Middecadal Global Landsat Survey for 2005-2006 is extending previous 1990 and 2000 efforts for global, high-quality Landsat data. Despite this progress, essential challenges for building a sustained global land-cover-observing system remain, including: international cooperation on the continuity of global observations; ensuring consistency in land monitoring approaches; community engagement and country participation in mapping activities; commitment to ongoing quality assurance and validation; and regional networking and capacity building.

The task objectives of this reporting phase included: (1) completing the draft of the LST Algorithms Theoretical Basic Document by July 30, 1993; (2) making a detailed characterization of the thermal infrared measurement system including spectrometer, blackbody, and radiation sources; (3) making TIR spectral measurements of water and snow-cover surfaces with the MIDAC M2401 spectrometer; and (4) making conceptual and engineering design of an accessory system for spectrometric measurements at variable angles. These objectives are based on the requirements by the MODIS Science Team and the unique challenge in the development of MODIS LST algorithms: to acquire accurate spectral emissivity data of land covers in the near-term and to make ground validations of the LST product in the long-term with a TIR measurement system.

Increasing attention has been devoted to the occurence of drought kill in forests worldwide. These mortality events are significant disruptions to the terrestrial carbon cycle, but the mechanisms required to represent drought kill are not represented in terrestrial carbon cycle models. In part, this is due to the challenge of representing the diversity of hydraulic strategies, which include stomatal sensitivity to water deficit and woody tissue vulnerability to cavitation at low water potential. In part, this is due to the challenge of representing this boundary value problem numerically, because the hydraulic components determine water potential at the leaf, but the stomatal conductance on the leaf also determines the hydraulic gradients within the plant. This poster will describe the development of a landsurface model parameterization of diverse tree hydraulic strategies.

A global intercomparison of 12 monthly mean landsurface heat flux products for the period 1993-1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line landsurface models (GSWP-2, GLDAS CLM/ Mosaic/Noah). An intercomparison of the global latent heat flux (Q(sub le)) annual means shows a spread of approx 20 W/sq m (all-product global average of approx 45 W/sq m). A similar spread is observed for the sensible (Q(sub h)) and net radiative (R(sub n)) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Q(sub le)and Q(sub h) absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Q(sub le) differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Q(sub le) and R(sub n) were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Depth to bedrock serves as the lower boundary of landsurface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of depth to bedrock (DTB). Observations were extracted from a global compilation of soil profile data (ca. 1,30,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surface reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forest and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250 m as an ensemble prediction of the two independently fitted models. The 10-fold cross-validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under-represented areas.

A technique has been developed for assimilating Geostationary Operational Environmental Satellite (GOES)-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The assimilation technique has been applied to the Oklahoma-Kansas region during the spring-summer 2000 time period when dynamic changes in vegetation cover occur. In April, central Oklahoma is characterized by large NDVI associated with winter wheat while surrounding areas are primarily rangeland with lower NDVI. In July the vegetation pattern reverses as the central wheat area changes to low NDVI due to harvesting and the surrounding rangeland is greener than it was in April. The goal of this study is to determine if assimilating satellite landsurface data can improve simulation of the complex spatial distribution of surface energy and water fluxes across this region. The PSU/NCAR NM5 V3 system is used in this study. The grid configuration consists of a 36-km CONUS domain and a 12-km nest over the area of interest. Bulk verification statistics (BIAS and RMSE) of surface

In 1998, the Multi-angle Imaging SpectroRadiometer (MISR) will fly aboard the EOS-AM1 spacecraft. MISR will enable unique methods for retrieving the properties of atmospheric aerosols, by providing global imagery of the Earth at nine viewing angles in four visible and near-IR spectral bands. As part of the MISR algorithm development, theoretical methods of analyzing multi-angle, multi-spectral data are being tested using images acquired by the airborne Advanced Solid-State Array Spectroradiometer (ASAS). In this paper we derive a method to be used over landsurfaces for retrieving the change in opacity between spectral bands, which can then be used in conjunction with an aerosol model to derive a bound on absolute opacity.

Soil moisture (SM) is a key parameter in agriculture, hydrology and ecology studies. The global SM retrievals have been providing by microwave remote sensing technology since late 1970s and many SM retrieval algorithms have been developed, calibrated and applied on satellite sensors such as AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System), AMSR-2 (Advanced Microwave Scanning Radiometer 2) and SMOS (Soil Moisture and Ocean Salinity). Particularly, SMAP (Soil Moisture Active/Passive) satellite, which was developed by NASA, was launched in January 2015. SMAP provides soil moisture products of 9 km and 36 km spatial resolutions which are not capable for research and applications of finer scale. Toward this issue, this study applied a SM disaggregation algorithm to disaggregate SMAP passive microwave soil moisture 36 km product. This algorithm was developed based on the thermal inertial relationship between daily surface temperature variation and daily average soil moisture which is modulated by vegetation condition, by using remote sensing retrievals from AVHRR (Advanced Very High Resolution Radiometer, MODIS (Moderate Resolution Imaging Spectroradiometer), SPOT (Satellite Pour l'Observation de la Terre), as well as LandSurface Model (LSM) output from NLDAS (North American Land Data Assimilation System). The disaggregation model was built at 1/8o spatial resolution on monthly basis and was implemented to calculate and disaggregate SMAP 36 km SM retrievals to 1 km resolution in Oklahoma. The SM disaggregation results were also validated using MESONET (Mesoscale Network) and MICRONET (Microscale Network) ground SM measurements.

For planetary lander missions, the most challenging phase of the spacecraft to ground communications is during the entry, descent, and landing (EDL). As each 2003 Mars Exploration Rover (MER) enters the Martian atmosphere, it slows dramatically. The extreme acceleration and jerk cause extreme Doppler dynamics on the X-band signal received on Earth. When the vehicle slows sufficiently, the parachute is deployed, causing almost a step in deceleration. After parachute deployment, the lander is lowered beneath the parachute on a bridle. The swinging motion of the lander imparts high Doppler dynamics on the signal and causes the received signal strength to vary widely, due to changing antenna pointing angles. All this time, the vehicle transmits important health and status information that is especially critical if the landing is not successful. Even using the largest Deep Space Network antennas, the weak signal and high dynamics render it impossible to conduct reliable phase coherent communications. Therefore, a specialized form of frequency-shift-keying will be used. This paper describes the EDL scenario, the signal conditions, the methods used to detect and frequency-track the carrier and to detect the data modulation, and the resulting performance estimates.

Undergraduate students conducted a semester-long research project as part of a special topics course that launched the Austin College Weather Station in spring 2001. The weather station is located on restored prairie roughly 100 km north of Dallas, Texas. In addition to standard meteorological observations, the Austin College Weather Station measures surface quantities such as soil moisture, soil temperature, solar radiation, infrared radiation, and soil heat flux. These additional quantities are used to calculate the surface energy balance using the Bowen ratio method. Thus, the Austin College Weather Station provides valuable information on land-atmosphere interaction in a prairie environment. This project provided a remarkable learning experience for the students. Each student supervised two instruments on the weather station. Students skillfully learned instrumentation details and the physical phenomena measured by the instruments. Team meetings were held each week to discuss issues such as station location, power requirements, telecommunication options, and data acquisition. Students made important decisions during the meetings. They would then work collaboratively on specific tasks that needed to be accomplished before the next meeting. Students also assessed the validity of their measurements after the weather station came on-line. With this approach, students became the experts. They utilized the scientific method to think critically and to solve problems. For at least a semester, students became Earth system scientists.

NASA's vast array of scientific data within its Distributed Active Archive Centers (DAACs) is especially valuable to both traditional research scientists as well as the emerging market of Earth Science Information Partners. For example, the air quality science and management communities are increasingly using satellite derived observations in their analyses and decision making. The Air Quality Cluster in the Federation of Earth Science Information Partners (ESIP) uses web infrastructures of interoperability, or Service Oriented Architecture (SOA), to extend data exploration, use, and analysis and provides a user environment for DAAC products. In an effort to continually offer these NASA data to the broadest research community audience, and reusing emerging technologies, both NASA's Goddard Earth Science (GES) and Land Process (LP) DAACs have engaged in a web services pilot project. Through these projects both GES and LP have exposed data through the Open Geospatial Consortiums (OGC) Web Services standards. Reusing several different existing applications and implementation techniques, GES and LP successfully exposed a variety data, through distributed systems to be ingested into multiple end-user systems. The results of this project will enable researchers world wide to access some of NASA's GES & LP DAAC data through OGC protocols. This functionality encourages inter-disciplinary research while increasing data use through advanced technologies. This paper will concentrate on the implementation and use of OGC Web Services, specifically Web Map and Web Coverage Services (WMS, WCS) at GES and LP DAACs, and the value of these services within scientific applications, including integration with the DataFed air quality web infrastructure and in the development of data analysis web applications.

The Landsat legacy spans more than forty years of moderate resolution, multi-spectral imaging of the Earth's surface. Applications for Landsat data include global environmental change, disaster planning and recovery, crop and natural resource management, and glaciology. In recent years, coastal water science has been greatly enhanced by the outstanding on-orbit performance of Landsat 8. Ball Aerospace designed and built the Operational Land Imager (OLI) instrument on Landsat 8, and is in the process of building OLI 2 for Landsat 9. Both of these instruments have the same design however improved performance is expected from OLI 2 due to greater image bit depth (14 bit on OLI 2 vs 12 bit on OLI). Ball Aerospace is currently working on two novel instrument architectures applicable to Sustainable Land Imaging for Landsat 10 and beyond. With increased budget constraints probable for future missions, technological improvements must be included in future instrument architectures to enable increased capabilities at lower cost. Ball presents the instrument architectures and associated capabilities enabling new science in past, current, and future Landsat missions.

In this study a set of landsurface data assimilation (DA) experiments making use of satellite derived soil moisture (SM) are presented. These experiments have two objectives: (1) to test the information content of satellite remote sensing of soil moisture for numerical weather prediction (NWP) models, and (2) to test a simplified assimilation of these data through the use of a Neural Network (NN) retrieval. Advanced Scatterometer (ASCAT) and Soil Moisture and Ocean Salinity (SMOS) data were used. The SMOS soil moisture dataset was obtained specifically for this project training a NN using SMOS brightness temperatures as input and using as reference for the training European Centre for Medium-Range Weather Forecasts (ECMWF) H-TESSEL SM fields. In this way, the SMOS NN SM dataset has a similar climatology to that of the model and it does not present a global bias with respect to the model. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL landsurface model. This system is very computationally efficient and allows to perform long surface assimilation experiments (one whole year, 2012). SMOS NN SM DA experiments are compared to ASCAT SM DA experiments. In both cases, experiments with and without 2 m air temperature and relative humidity DA are discussed using different observation errors for the ASCAT and SMOS datasets. Seasonal, geographical and soil-depth-related differences between the results of those experiments are presented and discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives in general similar results to the model open loop with no assimilation even if significant differences can be seen for specific sites with in situ measurements. The sensitivity to observation errors to the SM dataset slightly differs depending on the networks of in situ measurements, however it is relatively low for the tests

New York State Education Dept., Albany. Bureau of General Education Curriculum Development.

This syllabus begins with a list of program objectives and performance criteria for the study of three general topic areas in earth science and a list of 22 science processes. Following this information is a listing of concepts and understandings for subtopics within the general topic areas: (1) the earth's surface--surface features, rock…

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and landsurface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of landsurface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty module in NASA's Land Information System (LIS-OPT), whereby parameter sets are calibrated in the Noah landsurface model and classified according to the land cover and soil type mapping of the observations and the full domain. The impact of the calibrated parameters on the a) spin up of landsurface states used as initial conditions, and b) heat and moisture fluxes of the coupled (LIS-WRF) simulations are then assessed in terms of ambient weather, PBL budgets, and precipitation along with L-A coupling diagnostics. In addition, the sensitivity of this approach to the period of calibration (dry, wet, normal) is investigated. Finally, tradeoffs of computational tractability and scientific validity (e.g.,. relating to the representation of the spatial dependence of parameters) and the feasibility of calibrating to multiple observational datasets are also discussed.

The possibility of observing shallow groundwater depth and areal extent using satellite measurements can support groundwater models and vast irrigation systems management. Besides, these measurements help to integrate groundwater effects on surface energy balance within landsurface models and clima...

Landsurface modeling (LSM) is important in WRF/CMAQ for simulating the exchange of heat, moisture, momentum, trace atmospheric chemicals, and windblown dust between the landsurface and the atmosphere.? Vegetation and soil treatments are crucial in LSM for surface energy budgets...

Earth System Science is a discipline that performs holistic and comprehensive research on various components of the Earth. One of a key issue for the Earth monitoring and observation is to enhance the observation duration, the time intervals during which the Earthsurface features can be observed by sensors. In this work, we propose to utilise the Moon as an Earth observation platform. Thanks to the long distance between the Earth and the Moon, and the vast space on the lunar surface which is suitable for sensor installation, this Earth observation platform could have large spatial coverage, long temporal duration, and could perform multi-layer detection of the Earth. The line of sight between a proposed Moon-based platform and the Earth will change with different lunar surface positions; therefore, in this work, the position of the lunar surface was divided into four regions, including one full observation region and three incomplete observation regions. As existing methods are not able to perform global-scale observations, a Boolean matrix method was established to calculate the necessary observation durations from a Moon-based platform. Based on Jet Propulsion Laboratory (JPL) ephemerides and Earth Orientation Parameters (EOP), a formula was developed to describe the geometrical relationship between the Moon-based platform and Earthsurface features in the unified spatial coordinate system and the unified time system. In addition, we compared the observation geometries at different positions on the lunar surface and two parameters that are vital to observation duration calculations were considered. Finally, an analysis method was developed. We found that the observation duration of a given Earthsurface feature shows little difference regardless of sensor position within the full observation region. However, the observation duration for sensors in the incomplete observation regions is reduced by at least half. In summary, our results demonstrate the suitability

pressure gradients, and modeled winds from weather re-analyses do not exhibit any comparable stilling trends than at surface stations. For instance, large-scale circulation changes captured in the most recent European Centre for Medium Range Weather Forecast re-analysis (ERA-interim) can only explain only up to 30% of the Eurasian wind stilling. In addition, a significant amount of the slow-down could originate from a generalized increase in surface roughness, due for instance to forest growth and expansion, and urbanization. This hypothesis is supported by theoretical calculations combined with meso-scale model simulations. For future wind power energy resource, the part of wind decline due to land cover changes is easier to cope with than that due to global atmospheric circulation slow down.

The triggering of the land breeze, and hence the development of deep convection over heterogeneous land should be understood as a consequence of the complex processes involving various factors from landsurface and atmosphere simultaneously. That is a sub-grid scale process that many large-scale models have difficulty incorporating it into the parameterization scheme partly due to lack of our understanding. Thus, it is imperative that we approach the problem using a high-resolution modeling framework. In this study, we use SAM-SLM (Lee and Khairoutdinov, 2015), a large-eddy simulation model coupled to a land model, to explore the cloud effect such as cold pool, the cloud shading and the soil moisture memory on the land breeze structure and the further development of cloud and precipitation over a heterogeneous landsurface. The atmospheric large scale forcing and the initial sounding are taken from the new composite case study of the fair-weather, non-precipitating shallow cumuli at ARM SGP (Zhang et al., 2017). We model the landsurface as a chess board pattern with alternating leaf area index (LAI). The patch contrast of the LAI is adjusted to encompass the weak to strong heterogeneity amplitude. The surface sensible- and latent heat fluxes are computed according to the given LAI representing the differential surface heating over a heterogeneous landsurface. Separate from the surface forcing imposed from the originally modeled surface, the cases that transition into the moist convection can induce another layer of the surface heterogeneity from the 1) radiation shading by clouds, 2) adjusted soil moisture pattern by the rain, 3) spreading cold pool. First, we assess and quantifies the individual cloud effect on the land breeze and the moist convection under the weak wind to simplify the feedback processes. And then, the same set of experiments is repeated under sheared background wind with low level jet, a typical summer time wind pattern at ARM SGP site, to

Exoplanet discovery has made remarkable progress, with the first rocky planets having been detected in the central star’s liquid water habitable zone. The remote sensing techniques used to characterize such planets for potential habitability and life rely solely on our understanding of life on Earth. The vegetation red edge from terrestrial land plants is often used as a direct signature of life, but it occupies only a small niche in the environmental parameter space that binds life on present-day Earth and has been widespread for only about 460 My. To more fully exploit the diversity of the one example of life known, we measured the spectral characteristics of 137 microorganisms containing a range of pigments, including ones isolated from Earth’s most extreme environments. Our database covers the visible and near-infrared to the short-wavelength infrared (0.35–2.5 µm) portions of the electromagnetic spectrum and is made freely available from biosignatures.astro.cornell.edu. Our results show how the reflectance properties are dominated by the absorption of light by pigments in the visible portion and by strong absorptions by the cellular water of hydration in the infrared (up to 2.5 µm) portion of the spectrum. Our spectral library provides a broader and more realistic guide based on Earth life for the search for surface features of extraterrestrial life. The library, when used as inputs for modeling disk-integrated spectra of exoplanets, in preparation for the next generation of space- and ground-based instruments, will increase the chances of detecting life. PMID:25775594

Advances in sensor capabilities, but also in electronics, optics, RF communication, and off-the-grid power are enabling new measurement paradigms. NCAR's Earth Observing Laboratory (EOL) is considering new sensors, new deployment modes, and integrated observing strategies to address challenges in understanding within the atmospheric boundary layer and the underlying coupling to the landsurface. Our vision is of a network of deployable observing sites, each with a suite of complementary instruments that measure surface-atmosphere exchange, and the state and evolution of the boundary layer. EOL has made good progress on distributed surface energy balance and flux stations, and on boundary layer remote sensing of wind and water vapor, all suitable for deployments of combined instruments and as network of such sites. We will present the status of the CentNet surface network development, the 449-MHz modular wind profiler, and a water vapor and temperature profiling differential absorption lidar (DIAL) under development. We will further present a concept for a test bed to better understand the value of these and other possible instruments in forming an instrument suite flexible for multiple research purposes.

Depth to bedrock serves as the lower boundary of landsurface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of Depth to bedrock (DTB). Observations were extracted from a global compilation of soil profile data (ca. 130,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surfacee reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forests and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250m as an ensemble prediction of the two independently fitted models. The 10-fold cross-validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under-represented areas.

By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several landsurface parameters including impervious surfaces and landsurface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Landsurface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Landsurface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban landsurface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.

The spring of the year 2011 has been exceptionally dry in Western Europe. Over France, May 2011 has been one of the driest over the last 50 years. This event had a marked impact on vegetation development leading to very low value of the Leaf Area Index (LAI) during the growing season . In contrast, July 2011 has been in general wet and cold allowing a new vegetation development. This extreme event, followed by higher than normal rainfall is an excellent case-study to evaluate the capacity of a landsurface model to simulate the drought impact on vegetation, and vegetation recovery after a drought. In this study, we used the SURFEX landsurface model, in its ISBA-CC (CC stands for Carbon Cycle) configuration. The ISBA-CC version simulates the vegetation carbon cycle, interactive LAI and the carbon accumulation in wood and in the soil organic matter. This model is used by the GEOLAND2 Land Carbon Core Information Service. We performed 20-years simulations of SURFEX at high resolution (8 km) with atmospheric forcing from the SAFRAN dataset, an operational product over France. The vegetation map is provided by the ECOCLIMAP2 database. Following previous work that have confirmed a good simulation of the LAI inter-annual variability, this study investigates the ability of the model of reproducing the observed anomalies of LAI in 2011, in terms of timing and spatial patterns. We compare the simulated LAI with long time series (10 yr) of LAI derived from Earth Observation product within GEOLAND2 BIOPAR project. We quantify the anomalies of energy, water and carbon fluxes. We investigate the robustness of these results and the impact of modifying several important sub-modules of the model: soil texture, photosynthesis, and rainfall interception.

Important problems that confront future scientific exploration of Mars include the physical properties of Martian surface materials and the geologic processes that formed the materials. The design of landing spacecraft, roving vehicles, and sampling devices and the selection of landing sites, vehicle traverses, and sample sites will be, in part, guided by the physical properties of the materials. Four materials occur in the sample fields of the Viking landers: (1) drift, (2) crusty to cloddy, (3) blocky, and (4) rock. The first three are soillike. Drift materials is weak, loose, and porous. We estimate that it has a dielectric constant near 2.4 and a thermal inertia near 1 ?? 10-3 to 3 ?? 10-3 (cal cm-2 sec 1 2 K-1) because of its low bulk density, fine grain size, and small cohesion. Crusty to cloddy material is expected to have a dielectric constant near 2.8 and a thermal inertia near 4 ?? 10-3 to 7 ?? 10-3 because of its moderate bulk density and cementation of grains. Blocky material should have a dielectric constant near 3.3 and a thermal inertia near 7 ?? 10-3 to 9 ?? 10-3 because of its moderate bulk density and cementation. Common basaltic rocks have dielectric constans near 8 and thermal inertias near 30 ?? 10-3 to 60 ?? 10-3. Comparisons of estimated dielectric constants and thermal inertias of the materials at the landing sites with those obtained remotely by Earth-based radars and Viking Orbiter thermal sensors suggest that the materials at the landing sites are good analogs for materials elsewhere on Mars. Correlation of remotely estimated dielectric constant and thermal inertias indicates two modal values for paired values of dielectric constants and thermal inertias near (A) 2 and 2 ?? 10-3 and (B) 3 and 6 ?? 10-3, respectively. These two modes are comparable to the dielectric constants and thermal inertias for drift and crusty to cloddy material, respectively. Dielectric constants and thermal inertias for blocky material are larger but conistent

Dry deposition is a key component of surface-atmosphere exchange of compounds, acting as a sink for several chemical species. Meteorological factors, chemical properties of the trace gas considered and landsurface properties are strong drivers of dry deposition efficiency and variability. Under both climatic and anthropogenic pressure, the vegetation distribution over the Earth has been changing a lot over the past centuries, and could be significantly altered in the future. In this study, we perform a modeling investigation of the potential impact of land-cover changes between present-day (2006) and the future (2050) on dry deposition rates, with special interest for ozone (O3) and nitric acid vapor (HNO3), two compounds which are characterized by very different physico-chemical properties. The 3-D chemistry transport model LMDz-INCA is used, considering changes in vegetation distribution based on the three future projections RCPs 2.6, 4.5 and 8.5. The 2050 RCP 8.5 vegetation distribution leads to a rise up to 7 % (+0.02 cm s-1) in VdO3 and a decrease of -0.06 cm s-1 in VdHNO3 relative to the present day values in tropical Africa, and up to +18 and -15 % respectively in Australia. When taking into account the RCP 4.5 scenario, which shows dramatic land cover change in Eurasia, VdHNO3 increases by up to 20 % (annual-mean value) and reduces VdO3 by the same magnitude in this region. When analyzing the impact of dry deposition change on atmospheric chemical composition, our model calculates that the effect is lower than 1 ppb on annual mean surface ozone concentration, for both for the RCP8.5 and RCP2.6 scenarios. The impact on HNO3 surface concentrations is more disparate between the two scenarios, regarding the spatial repartition of effects. In the case of the RCP 4.5 scenario, a significant increase of the surface O3 concentration reaching locally up to 5 ppb (+5 %) is calculated on average during the June-August period. This scenario induces also an increase of

LandSurface Temperature and Emissivity (LST&E) are an important Earth System Data Record (ESDR) and Environmental Climate Variable (ECV) defined by NASA and GCOS respectively. LST&E data are key variables used in land cover/land use change studies, in surface energy balance and atmospheric water vapor retrieval models and retrievals, and in climate research. LST&E products are currently produced on a routine basis using data from the MODIS instruments on the NASA EOS platforms and by the VIIRS instrument on the Suomi-NPP platform that serves as a bridge between NASA EOS and the next-generation JPSS platforms. Two new NASA LST&E products for MODIS (MxD21) and VIIRS (VNP21) are being produced during 2017 using a new approach that addresses discrepancies in accuracy and consistency between the current suite of split-window based LST products. The new approach uses a Temperature Emissivity Separation (TES) algorithm, originally developed for the ASTER instrument, to physically retrieve both LST and spectral emissivity consistently for both sensors with high accuracy and well defined uncertainties. This study provides a rigorous assessment of accuracy of the MxD21/VNP21 products using temperature- and radiance-based validation strategies and demonstrates continuity between the products using collocated matchups over CONUS. We will further demonstrate potential science use of the new products with studies related to heat waves, monitoring snow melt dynamics, and land cover/land use change.

Developed by the Hydrological Sciences Laboratory at NASA Goddard Space Flight Center (GSFC), the Land Information System (LIS) is a high-performance software framework for terrestrial hydrology modeling and data assimilation. LIS provides the ability to integrate satellite and ground-based observational products and advanced modeling algorithms to extract landsurface states and fluxes. Through a partnership with the National Center for Atmospheric Research (NCAR) and the University of Washington, the LIS model is currently being extended to include the Structure for Unifying Multiple Modeling Alternatives (SUMMA). With the addition of SUMMA in LIS, meaningful simulations containing a large multi-model ensemble will be enabled and can provide advanced probabilistic continental-domain modeling capabilities at spatial scales relevant for water managers. The resulting LIS/SUMMA application framework is difficult for non-experts to install due to the large amount of dependencies on specific versions of operating systems, libraries, and compilers. This has created a significant barrier to entry for domain scientists that are interested in using the software on their own systems or in the cloud. In addition, the requirement to support multiple run time environments across the LIS community has created a significant burden on the NASA team. To overcome these challenges, LIS/SUMMA has been deployed using Linux containers, which allows for an entire software package along with all dependences to be installed within a working runtime environment, and Kubernetes, which orchestrates the deployment of a cluster of containers. Within a cloud environment, users can now easily create a cluster of virtual machines and run large-scale LIS/SUMMA simulations. Installations that have taken weeks and months can now be performed in minutes of time. This presentation will discuss the steps required to create a cloud-enabled large-scale simulation, present examples of its use, and

The LandSurface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of landsurface, snow, and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth System Models (ESMs). The solid and liquid water stored at the landsurface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and landsurface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems).The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (LMIP, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (LFMIP, building upon the GLACE-CMIP blueprint).

The LandSurface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of landsurface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). Furthermore, the solid and liquid water stored at the landsurface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and landsurface memory. They both stronglymore » affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. But, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (“LMIP”, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (“LFMIP”, building upon the GLACE-CMIP blueprint).« less

The LandSurface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of landsurface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). Furthermore, the solid and liquid water stored at the landsurface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and landsurface memory. They both stronglymore » affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. But, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (“LMIP”, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (“LFMIP”, building upon the GLACE-CMIP blueprint).« less

Displacement of mass of limited deformability ("solids") on the Earth's surface is opposed by friction and (the analog of) form resistance - impediments relaxed by rotational motion, self-powering of mass units, and transport infrastructure. These features of solids transport first evolved in the biosphere prior to the emergence of technology, allowing slope-independent, diffusion-like motion of discrete objects as massive as several tons, as illustrated by animal foraging and movement along game trails. However, high-energy-consumption technology powered by fossil fuels required a mechanism that could support fast advective transport of solids, i.e., long-distance, high-volume, high-speed, unidirectional, slope-independent transport across the landsurface of materials like coal, containerized fluids, minerals, and economic goods. Pre-technology nature was able to sustain regional- and global-scale advection only in the limited form of piggybacking on geophysical flows of water (river sediment) and air (dust). The appearance of a mechanism for sustained advection of solids independent of fluid flows and gravity appeared only upon the emergence of human purpose. Purpose enables solids advection by, in effect, simulating a continuous potential gradient, otherwise lacking, between discrete and widely separated fossil-fuel energy sources and sinks. Invoking purpose as a mechanism in solids advection is an example of the need to import anthropic principles and concepts into the language and methodology of modern Earth system dynamics. As part of the emergence of a generalized solids advection mechanism, several additional transport requirements necessary to the function of modern large-scale technological systems were also satisfied. These include spatially accurate delivery of advected payload, targetability to essentially arbitrarily located destinations (such as cities), and independence of structure of advected payload from transport mechanism. The latter property

Displacement of mass of limited deformability ("solids") on the Earth's surface is opposed by friction and (the analog of) form resistance - impediments relaxed by rotational motion, self-powering of mass units, and transport infrastructure. These features of solids transport first evolved in the biosphere prior to the emergence of technology, allowing slope-independent, diffusion-like motion of discrete objects as massive as several tons, as illustrated by animal foraging and movement along game trails. However, high-energy-consumption technology powered by fossil fuels required a mechanism that could support advective transport of solids, i.e., long-distance, high-volume, high-speed, unidirectional, slope independent transport across the landsurface of materials like coal, containerized fluids, and minerals. Pre-technology nature was able to sustain large-scale, long-distance solids advection only in the limited form of piggybacking on geophysical flows of water (river sediment) and air (dust). The appearance of a generalized mechanism for advection of solids independent of fluid flows and gravity appeared only upon the emergence of human purpose. Purpose enables solids advection by, in effect, enabling a simulated continuous potential gradient, otherwise lacking, between discrete and widely separated fossil-fuel energy sources and sinks. Invoking purpose as a mechanism in solids advection is an example of the need to import anthropic principles and concepts into the language and methodology of modern Earth system dynamics. As part of the emergence of a generalized solids advection mechanism, several additional transport requirements necessary to the function of modern large-scale technological systems were also satisfied. These include spatially accurate delivery of advected payload, targetability to essentially arbitrarily located destinations (such as cities), and independence of structure of advected payload from transport mechanism. The latter property

Co-registration of Optically Sensed Images and Correlation (COSI-Corr) is a software package developed at the California Institute of Technology (USA) for accurate geometrical processing of optical satellite and aerial imagery. Initially developed for the measurement of co-seismic ground deformation using optical imagery, COSI-Corr is now used for a wide range of applications in Earth Sciences, which take advantage of the software capability to co-register, with very high accuracy, images taken from different sensors and acquired at different times. As long as a sensor is supported in COSI-Corr, all images between the supported sensors can be accurately orthorectified and co-registered. For example, it is possible to co-register a series of SPOT images, a series of aerial photographs, as well as to register a series of aerial photographs with a series of SPOT images, etc... Currently supported sensors include the SPOT 1-5, Quickbird, Worldview 1 and Formosat 2 satellites, the ASTER instrument, and frame camera acquisitions from e.g., aerial survey or declassified satellite imagery. Potential applications include accurate change detection between multi-temporal and multi-spectral images, and the calibration of pushbroom cameras. In particular, COSI-Corr provides a powerful correlation tool, which allows for accurate estimation of surface displacement. The accuracy depends on many factors (e.g., cloud, snow, and vegetation cover, shadows, temporal changes in general, steadiness of the imaging platform, defects of the imaging system, etc...) but in practice, the standard deviation of the measurements obtained from the correlation of mutli-temporal images is typically around 1/20 to 1/10 of the pixel size. The software package also includes post-processing tools such as denoising, destriping, and stacking tools to facilitate data interpretation. Examples drawn from current research in, e.g., seismotectonics, glaciology, and geomorphology will be presented. COSI-Corr is

The measurement of global precipitation, both rainfall and snowfall, is of critical importance to a wide range of users and applications. The fundamental means of measuring precipitation is the rain gauge. Although rain gauges have many drawbacks (including not measuring snowfall well), they remain the de facto source of precipitation information across the Earthsurface for hydro-meteorological purposes. While the accuracy and representative of each gauge can be assessed and monitored, a key limitation of rain and snow gauges is in their distribution across the globe. Gauges tend to be limited to the landsurface where their distribution and density is very variable, while over the oceans very few gauges are available and measurements available at island locations may not truly represent those of the surrounding oceans. The total numbers of gauges across the Earth, as noted in the literature, varies greatly primarily due to temporal sampling resolutions, periods of operation, the latency of the data and the availability of the data. These numbers range from a few thousand which are available in near real time, to an estimated hundreds of thousands if one includes all available 'official' gauges (this number might swell more if all amateur gauges are included, with crowdsourcing capable of providing even more). Considering those gauges that are routinely used in the generation of global precipitation products (i.e. those available and of reasonable quality), the physical area covered by rain gauges varies by a factor of about 25. Calculations suggest that if all available rain gauges are included, they would cover between 120 and 3,000 m2. For comparison, equivalent areas range from 267 m2 for the centre circle of a football (soccer) pitch, or about 260 m2 for a tennis court to about 3,000 m2 for half a football pitch. Each gauge should represent more than just the orifice of the gauge itself, however, observations and modelling suggest that the correlation

We made more tests of the version 2.0 daily Level 2 and Level 3 Land-Surface Temperature (LST) code (PGE 16) jointly with the MODIS Science Data Support Team (SDST). After making minor changes a few times, the PGE16 code has been successfully integrated and tested by MODIS SDST, and recently has passed the inspection at the Goddard Distributed Active Archive Center (DAAC). We conducted a field campaign in the area of Mono Lake, California on March 10, 1998, in order to validate the MODIS LST algorithm in cold and dry conditions. Two MODIS Airborne Simulator (MAS) flights were completed during the field campaign, one before noon, and another around 10 pm PST. The weather condition for the daytime flight was perfect: clear sky, the column water vapor measured by radiosonde around 0.3 cm, and wind speed less than a half meter per second. The quality of MAS data is good for both day and night flights. We analyzed the noise equivalent temperature difference (NE(delta)T) and the calibration accuracy of the seven MAS thermal infrared (TIR) bands, that are used in the MODIS day/night LST algorithm, with daytime MAS data over four flat homogeneous study areas: two on Grant Lake (covered with ice and snow, respectively), one on Mono Lake, and another on the snow field site where we made field measurements. NE(delta)T ranges from 0.2 to 0.6 k for bands 42, 45, 46, and 48. It ranges from 0.8 to 1.1 K for bands 30-32. The day and night MAS data have been used to retrieve surface temperature and emissivities in these bands. A simple method to correct the effect of night thin cirrus has been incorporated into the day/night LST algorithm in dry atmospheric conditions. We compared the retrieved surface temperatures with those measured with TIR spectrometer, radiometers and thermistors in the snow test site, and the retrieved emissivity images with topographic map. The daytime LST values match well within 1 K. The night LST retrieved from MAS data is 3.3 K colder than those from

Structure from Motion (SfM) has given researchers an invaluable tool for low-cost, high-resolution 3D mapping of the environment. These SfM 3D surface models are commonly constructed from many digital photographs collected with one digital camera (either handheld or attached to aerial platform). This method works for stationary or very slow moving objects. However, objects in motion are impossible to capture with one-camera SfM. With multiple simultaneously triggered cameras, it becomes possible to capture multiple photographs at the same time which allows for the construction 3D surface models of moving objects and surfaces, an instantaneous SfM (ISfM) surface model. In river science, ISfM provides a low-cost solution for measuring a number of river variables that researchers normally estimate or are unable to collect over large areas. With ISfM and sufficient coverage of the banks and RTK-GPS control it is possible to create a digital surface model of land and water surface elevations across an entire channel and water surface slopes at any point within the surface model. By setting the cameras to collect time-lapse photography of a scene it is possible to create multiple surfaces that can be compared using traditional digital surface model differencing. These water surface models could be combined the high-resolution bathymetry to create fully 3D cross sections that could be useful in hydrologic modeling. Multiple temporal image sets could also be used in 2D or 3D particle image velocimetry to create 3D surface velocity maps of a channel. Other applications in earth science include anything where researchers could benefit from temporal surface modeling like mass movements, lava flows, dam removal monitoring. The camera system that was used for this research consisted of ten pocket digital cameras (Canon A3300) equipped with wireless triggers. The triggers were constructed with an Arduino-style microcontroller and off-the-shelf handheld radios with a maximum

Increased demand for agricultural products and limited water supplies in Guanacaste, Costa Rica have encouraged the improvement of water management practices to increase resource use efficiency. Remotely sensed evapotranspiration (ET) data can contribute by providing insights into variables like crop health and water loss, as well as better inform the use of various irrigation techniques. EARTH University currently collects data in the region that are limited to costly and time-intensive in situ observations and will greatly benefit from the expanded spatial and temporal resolution of remote sensing measurements from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). In this project, Moderate Resolution Imaging Spectroradiometer (MODIS) Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) data, with a resolution of 5 km per pixel, was used to demonstrate to our partners at EARTH University the application of remotely sensed ET measurements. An experimental design was developed to provide a method of applying future ECOSTRESS data, at the higher resolution of 70 m per pixel, to research in managing and implementing sustainable farm practices. Our investigation of the diurnal cycle of landsurface temperature, net radiation, and evapotranspiration will advance the model science for ECOSTRESS, which will be launched in 2018 and installed on the International Space Station.

Weather and climate models must continue to increase in both resolution and complexity in order that forecasts become more accurate and reliable. Moving to lower numerical precision may be an essential tool for coping with the demand for ever increasing model complexity in addition to increasing computing resources. However, there have been some concerns in the weather and climate modelling community over the suitability of lower precision for climate models, particularly for representing processes that change very slowly over long time-scales. These processes are difficult to represent using low precision due to time increments being systematically rounded to zero. Idealised simulations are used to demonstrate that a model of deep soil heat diffusion that fails when run in single precision can be modified to work correctly using low precision, by splitting up the model into a small higher precision part and a low precision part. This strategy retains the computational benefits of reduced precision whilst preserving accuracy. This same technique is also applied to a full complexity landsurface model, resulting in rounding errors that are significantly smaller than initial condition and parameter uncertainties. Although lower precision will present some problems for the weather and climate modelling community, many of the problems can likely be overcome using a straightforward and physically motivated application of reduced precision.

We applied the multi-method strategy of land-surface temperature (LST) and emissivity measurements in two field campaigns this year for validating the MODIS LST algorithm. The first field campaign was conducted in Death Valley, CA, on March 3rd and the second one in Railroad Valley, NV, on June 23-27. ER2 MODIS Airborne Simulator (MAS) data were acquired in morning and evening for these two field campaigns. TIR spectrometer, radiometer, and thermistor data were also collected in the field campaigns. The LST values retrieved from MAS data with the day/night LST algorithm agree with those obtained from ground-based measurements within 1 C and show close correlations with topographic maps. The band emissivities retrieved from MAS data show close correlations with geological maps in the Death Valley field campaign. The comparison of measurement data in the latest Railroad Valley field campaign indicates that we are approaching the goals of the LST validation: LST uncertainty less than 0.5 C, and emissivity uncertainty less than 0.005 in the 10-13 spectral range. Measurement data show that the spatial variation in LST is the major uncertainty in the LST validation. In order to reduce this uncertainty, a new component of the multi-method strategy has been identified.

Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate–carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimationmore » system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model–data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. Furthermore, the new improved parameters for JULES are presented along with the associated uncertainties for each parameter.« less

Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model-data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

Discover Earth is a NASA-sponsored project for teachers of grades 5-12, designed to: (1) enhance understanding of the Earth as an integrated system; (2) enhance the interdisciplinary approach to science instruction; and (3) provide classroom materials that focus on those goals. Discover Earth is conducted by the Institute for Global Environmental Strategies in collaboration with Dr. Eric Barron, Director, Earth System Science Center, The Pennsylvania State University; and Dr. Robert Hudson, Chair, the Department of Meteorology, University of Maryland at College Park. The enclosed materials: (1) represent only part of the Discover Earth materials; (2) were developed by classroom teachers who are participating in the Discover Earth project; (3) utilize an investigative approach and on-line data; and (4) can be effectively adjusted to classrooms with greater/without technology access. The Discover Earth classroom materials focus on the Earth system and key issues of global climate change including topics such as the greenhouse effect, clouds and Earth's radiation balance, surface hydrology and land cover, and volcanoes and climate change. All the materials developed to date are available on line at (http://www.strategies.org) You are encouraged to submit comments and recommendations about these materials to the Discover Earth project manager, contact information is listed below. You are welcome to duplicate all these materials.

Landsurface temperature (LST) is one of the key states of the Earthsurface system. Remote sensing has the capability to obtain high-frequency LST observations with global coverage. However, mainly due to cloud cover, there are always gaps in the remotely sensed LST product, which hampers the application of satellite-based LST in data-driven modeling of surface energy and water exchange processes. We explored the suitability of the data interpolating empirical orthogonal functions (DINEOF) method in moderate resolution imaging spectroradiometer LST reconstruction around Ali on the Tibetan Plateau. To validate the reconstruction accuracy, synthetic clouds during both daytime and nighttime are created. With DINEOF reconstruction, the root mean square error and bias under synthetic clouds in daytime are 4.57 and -0.0472 K, respectively, and during the nighttime are 2.30 and 0.0045 K, respectively. The DINEOF method can well recover the spatial pattern of LST. Time-series analysis of LST before and after DINEOF reconstruction from 2002 to 2016 shows that the annual and interannual variabilities of LST can be well reconstructed by the DINEOF method.

Modelling of LandSurface Temperature is essential for short term and long term management of environmental studies and management activities of the Earth's resources. The objective of this research is to estimate and model LandSurface Temperatures (LST). For this purpose, Landsat 7 ETM+ images period from 2007 to 2012 were used for retrieving LST and processed through MATLAB software using Mamdani fuzzy inference systems (MFIS), which includes pre-monsoon and post-monsoon LST in the fuzzy model. The Mangalore City of Karnataka state, India has been taken for this research work. Fuzzy model inputs are considered as the pre-monsoon and post-monsoon retrieved temperatures and LST was chosen as output. In order to develop a fuzzy model for LST, seven fuzzy subsets, nineteen rules and one output are considered for the estimation of weekly mean air temperature. These are very low (VL), low (L), medium low (ML), medium (M), medium high (MH), high (H) and very high (VH). The TVX (Surface Temperature Vegetation Index) and the empirical method have provided estimated LST. The study showed that the Fuzzy model M4/7-19-1 (model 4, 7 fuzzy sets, 19 rules and 1 output) which developed over Mangalore City has provided more accurate outcomes than other models (M1, M2, M3, M5). The result of this research was evaluated according to statistical rules. The best correlation coefficient (R) and root mean squared error (RMSE) between estimated and measured values for pre-monsoon and post-monsoon LST found to be 0.966 - 1.607 K and 0.963- 1.623 respectively.

Topography has major control on landsurface processes through its influence on atmospheric forcing, soil and vegetation properties, network topology and drainage area. Consequently, accurate climate and landsurface simulations in mountainous regions cannot be achieved without considering the effects of topographic spatial heterogeneity. To test a computationally less expensive hyper-resolution landsurface modeling approach, we developed topography-based landunits within a hierarchical subgrid spatial structure to improve representation of landsurface processes in the ACME Land Model (ALM) with minimal increase in computational demand, while improving the ability to capture the spatial heterogeneity of atmospheric forcing and land cover influenced by topography. This study focuses on evaluation of the impacts of the new spatial structures on modeling landsurface processes. As a first step, we compare ALM simulations with and without subgrid topography and driven by grid cell mean atmospheric forcing to isolate the impacts of the subgrid topography on the simulated landsurface states and fluxes. Recognizing that subgrid topography also has important effects on atmospheric processes that control temperature, radiation, and precipitation, methods are being developed to downscale atmospheric forcings. Hence in the second step, the impacts of the subgrid topographic structure on landsurface modeling will be evaluated by including spatial downscaling of the atmospheric forcings. Preliminary results on the atmospheric downscaling and the effects of the new spatial structures on the ALM simulations will be presented.

LANCE (Land, Atmosphere Near-Real-Time Capability for EOS) in 2009. LANCE consists of special processing elements, co-located with selected EOSDIS data centers and processing facilities. A primary goal of LANCE is to bring multiple near-real-time systems under one umbrella, offering commonality in data access, quality control, and latency. LANCE now processes and distributes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric Infrared Sounder (AIRS), Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) instruments within 3 hours of satellite observation. The Rapid Response System and the Fire Information for Resource Management System (FIRMS) capabilities will be incorporated into LANCE in 2011. LANCE maintains a central website to facilitate easy access to data and user services. LANCE products are extensively tested and compared with science products before being made available to users. Each element also plans to implement redundant network, power and server infrastructure to ensure high availability of data and services. Through the user registration system, users are informed of any data outages and when new products or services will be available for access. Building on a significant investment by NASA in developing science algorithms and products, LANCE creates products that have a demonstrated utility for applications requiring near-real-time data. From lower level data products such as calibrated geolocated radiances to higher-level products such as sea ice extent, snow cover, and cloud cover, users have integrated LANCE data into forecast models and decision support systems. The table above shows the current near-real-time product categories by instrument. The ESDIS Project continues to improve the LANCE system and use the experience gained through practice to seek adjustments to improve the quality and performance of the system. For example, an

Parameterization of land-surface processes and consideration of surface inhomogeneities are very important to mesoscale meteorological modeling applications, especially those that provide information for air quality modeling. To provide crucial, reliable information on the diurn...

A model is developed for the large-eddy simulation (LES) of heterogeneous atmosphere and land-surface processes. This couples a LES model with a land-surface scheme. New developments are made to the land-surface scheme to ensure the adequate representation of atmosphere-land-surface transfers on the large-eddy scale. These include, (1) a multi-layer canopy scheme; (2) a method for flux estimates consistent with the large-eddy subgrid closure; and (3) an appropriate soil-layer configuration. The model is then applied to a heterogeneous region with 60-m horizontal resolution and the results are compared with ground-based and airborne measurements. The simulated sensible and latent heat fluxes are found to agree well with the eddy-correlation measurements. Good agreement is also found in the modelled and observed net radiation, ground heat flux, soil temperature and moisture. Based on the model results, we study the patterns of the sensible and latent heat fluxes, how such patterns come into existence, and how large eddies propagate and destroy land-surface signals in the atmosphere. Near the surface, the flux and land-use patterns are found to be closely correlated. In the lower boundary layer, small eddies bearing land-surface signals organize and develop into larger eddies, which carry the signals to considerably higher levels. As a result, the instantaneous flux patterns appear to be unrelated to the land-use patterns, but on average, the correlation between them is significant and persistent up to about 650 m. For a given land-surface type, the scatter of the fluxes amounts to several hundred W { m }^{-2}, due to (1) large-eddy randomness; (2) rapid large-eddy and surface feedback; and (3) local advection related to surface heterogeneity.

This viewgraph presentation reviews the use Adaptive Mesh Refinement (AMR) in simulating the Crustal Dynamics of Earth's Surface. AMR simultaneously improves solution quality, time to solution, and computer memory requirements when compared to generating/running on a globally fine mesh. The use of AMR in simulating the dynamics of the Earth's Surface is spurred by future proposed NASA missions, such as InSAR for Earthsurface deformation and other measurements. These missions will require support for large-scale adaptive numerical methods using AMR to model observations. AMR was chosen because it has been successful in computation fluid dynamics for predictive simulation of complex flows around complex structures.

The majority of the population in southwest Virginia depends economically on coal mining. In 2011, coal mining generated $2,000,000 in tax revenue to Wise County alone. However, surface mining completely removes land cover and leaves the land exposed to erosion. The destruction of the forest cover directly impacts local species, as some are displaced and others perish in the mining process. Even though surface mining has a negative impact on the environment, land reclamation efforts are in place to either restore mined areas to their natural vegetated state or to transform these areas for economic purposes. This project aimed to monitor the progress of land reclamation and the effect on the return of local species. By incorporating NASA Earth observations, such as Landsat 8 Operational Land Imager (OLI) and Landsat 5 Thematic Mapper (TM), re-vegetation process in reclamation sites was estimated through a Time series analysis using the Normalized Difference Vegetation Index (NDVI). A continuous source of cloud free images was accomplished by utilizing the Spatial and Temporal Adaptive Reflectance Fusion Model (STAR-FM). This model developed synthetic Landsat imagery by integrating the high-frequency temporal information from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and high-resolution spatial information from Landsat sensors In addition, the Maximum Entropy Modeling (MaxENT), an eco-niche model was used to estimate the adaptation of animal species to the newly formed habitats. By combining factors such as land type, precipitation from Tropical Rainfall Measuring Mission (TRMM), and slope from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the MaxENT model produced a statistical analysis on the probability of species habitat. Altogether, the project compiled the ecological information which can be used to identify suitable habitats for local species in reclaimed mined areas.

In our current era of intensive earth observation the time is ripe to shift away from studies relying on single sensors or single products to the synergistic use of multiple sensors and products at complementary spatial, temporal, and spectral scales. The use of multiple time series can not only reveal hotspots of change in landsurface dynamics, but can indicate plausible proximate causes of the changes and suggest their possible consequences. Here we explore recent trends in the landsurface dynamics of exemplary semi-arid grasslands in the western hemisphere, including the shortgrass prairie of eastern Colorado and New Mexico, the sandhills prairie of Nebraska, the "savana gramineo-lenhosa" variety of cerrado in central Brazil, and the pampas of Argentina. Observational datasets include (1) NBAR-based vegetation indices, landsurface temperature, and evapotranspiration from MODIS, (2) air temperature, water vapor, and vegetation optical depth from AMSR-E and AMSR2, (3) surface air temperature, water vapor, and relative humidity from AIRS, and (4) surface shortwave, longwave, and total net flux from CERES. The spatial resolutions of these nested data include 500 m, 1000 m, 0.05 degree, 25 km, and 1 degree. We apply the nonparametric Seasonal Kendall trend test to each time series independently to identify areas of significant change. We then examine polygons of co-occurrence of significant change in two or more types of products using the surface radiation and energy budgets as guides to interpret the multiple changes. Changes occurring across broad areas are more likely to be of climatic origin; whereas, changes that are abrupt in space and time and of limited area are more likely anthropogenic. Results illustrate the utility of considering multiple remote sensing products as complementary views of landsurface dynamics.

Landsurface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the landsurface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest a